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: "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
  Abstract: Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements have been recently considered in indoor communication environments for focusing signal reflections to intended user locations. However, the current proofs of concept require complex operations for the RIS configuration, which are mainly realized via wired control connections. In this paper, we present a deep learning method for efficient online wireless configuration of RISs when deployed in indoor communication environments. According to the proposed method, a database of coordinate fingerprints is implemented during an offline training phase. This fingerprinting database is used to train the weights and bias of a properly designed Deep Neural Network (DNN), whose role is to unveil the mapping between the measured coordinate information at a user location and the configuration of the RIS's unit cells that maximizes this user's received signal strength. During the online phase of the presented method, the trained DNN is fed with the measured position information at the target user to output the optimal phase configurations of the RIS for signal power focusing on this intended location. Our realistic simulation results using ray tracing on a three dimensional indoor environment demonstrate that the proposed DNN-based configuration method exhibits its merits for all considered cases, and effectively increases the achievable throughput at the target user location.

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"
    - "intelligent reflecting surface assisted non orthogonal multiple access"
    - "weighted sum power maximization for intelligent reflecting surface aided swipt"

Connected through hyperedge HE-2 (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"
    - "intelligent reflecting surface design for mimo system by maximizing sum path gains"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "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-3 (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"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "deep learning for direct hybrid precoding in millimeter wave massive mimo systems"

Connected through hyperedge HE-4 (induced by source paper "reconfigurable intelligent surface assisted multiuser miso systems exploiting deep reinforcement learning"):
  with members:
    - "beamforming design for large scale antenna arrays using deep learning"
    - "learning to optimize training deep neural networks for interference management"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "large intelligent surface assisted wireless communication exploiting statistical csi"

Connected through hyperedge HE-5 (induced by source paper "programmable metasurface based multicast systems design and analysis"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "passive beamforming and information transfer via large intelligent surface"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "beamforming optimization for intelligent reflecting surface with discrete phase shifts"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning for passive beamforming"):
  with members:
    - "beamforming design for large scale antenna arrays using deep learning"
    - "learning to optimize training deep neural networks for interference management"
    - "achievable rate maximization by passive intelligent mirrors"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "reliability analysis of large intelligent surfaces liss rate distribution and outage probability"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "energy efficient multi user miso communication using low resolution large intelligent surfaces"
    - "capacity degradation with modeling hardware impairment in large intelligent surface"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "performance analysis of large intelligence surfaces liss asymptotic data rate and channel hardening effects"
    - "common codebook millimeter wave beam design designing beams for both sounding and communication with uniform planar arrays"

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)