human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "enabling large intelligent surfaces with compressive sensing and deep learning"
  Abstract: Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. In the second approach, we develop a deep learning based solution where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.

Connected through hyperedge HE-1 (induced by source paper "intelligent reflecting surface assisted secure wireless communications with multiple transmit and multiple receive antennas"):
  with members:
    - "secure wireless communication via intelligent reflecting surface"
    - "a survey of optimization approaches for wireless physical layer security"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "resource allocation for secure irs assisted multiuser miso systems"
    - "secure transmission strategy for intelligent reflecting surface enhanced wireless system"
    - "enabling secure wireless communications via intelligent reflecting surfaces"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "intelligent reflecting surface assisted non orthogonal multiple access"

Connected through hyperedge HE-2 (induced by source paper "millimeter wave communications with an intelligent reflector performance optimization and distributional reinforcement learning"):
  with members:
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "reflections in the sky millimeter wave communication with uav carried intelligent reflectors"
    - "reflect and transmit array antennas for scalable and energy efficient mmwave massive mimo"
    - "intelligent reflecting and transmitting surface aided millimeter wave massive mimo"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "performance analysis of large intelligent surfaces liss uplink spectral efficiency and pilot training"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "fractional programming for communication systems part i power control and beamforming"

Connected through hyperedge HE-3 (induced by source paper "parallel factor decomposition channel estimation in ris assisted multi user miso communication"):
  with members:
    - "holographic mimo surfaces for 6g wireless networks opportunities challenges and trends"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "passive beamforming and information transfer via large intelligent surface"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "intelligent reflecting surface design for mimo system by maximizing sum path gains"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "intelligent reflecting surface enhanced ofdm channel estimation and reflection optimization"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for intelligent reflecting surfaces towards standalone operation"):
  with members:
    - "reconfigurable intelligent surface empowered 6g wireless communications challenges and opportunities"
    - "deep reinforcement learning for 5g networks joint beamforming power control and interference coordination"
    - "holographic mimo surfaces for 6g wireless networks opportunities challenges and trends"
    - "deep learning for massive mimo with 1 bit adcs when more antennas need fewer pilots"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"

Connected through hyperedge HE-5 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems"):
  with members:
    - "closed loop sparse channel estimation for wideband millimeter wave full dimensional mimo systems"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "compressive sensing based channel estimation for millimeter wave full dimensional mimo with lens array"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "compressive sensing techniques for next generation wireless communications"
    - "channel estimation for millimeter wave massive mimo with hybrid precoding over frequency selective fading channels"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"

Connected through hyperedge HE-6 (induced by source paper "reconfigurable intelligent surface empowered 6g wireless communications challenges and opportunities"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "channel estimation for intelligent reflecting surface assisted multiuser communications"
    - "the roadmap to 6g ai empowered wireless networks"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"
    - "user assignment with distributed large intelligent surface lis systems"

Connected through hyperedge HE-7 (induced by source paper "capacity characterization for intelligent reflecting surface aided mimo communication"):
  with members:
    - "miso wireless communication systems via intelligent reflecting surfaces"
    - "secure wireless communication via intelligent reflecting surface"
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "resource allocation for secure irs assisted multiuser miso systems"
    - "intelligent reflecting surface meets ofdm protocol design and rate maximization"
    - "enabling secure wireless communications via intelligent reflecting surfaces"
    - "intelligent reflecting surface assisted non orthogonal multiple access"
    - "weighted sum power maximization for intelligent reflecting surface aided swipt"

Connected through hyperedge HE-8 (induced by source paper "asymptotic max min sinr analysis of reconfigurable intelligent surface assisted miso systems"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "asymptotic analysis of rzf over double scattering channels with mmse estimation"
    - "rfocus practical beamforming for small devices"
    - "resource allocation for secure irs assisted multiuser miso systems"
    - "enabling secure wireless communications via intelligent reflecting surfaces"
    - "mimo assisted networks relying on large intelligent surfaces a stochastic geometry model"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)