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: "intelligent reflecting and transmitting surface aided millimeter wave massive mimo"
  Abstract: In this paper, we study two novel massive MIMO architectures for millimeter wave (mmWave) communications which comprise few active antennas, each equipped with a dedicated radio frequency (RF) chain, that illuminate a nearby large intelligent reflecting/transmitting surface (IRS/ITS). The IRS (ITS) consists of a large number of low-cost and energy-efficient passive antenna elements which are able to reflect (transmit) a phase-shifted version of the incident electromagnetic field. Similar to lens array (LA) antennas, IRS/ITS-aided antenna architectures are energy efficient due to the almost lossless over-the-air connection between the active antennas and the intelligent surface. However, unlike for LA antennas, for which the number of active antennas has to linearly grow with the number of passive elements (i.e., the lens aperture) due to the non-reconfigurablility (i.e., non-intelligence) of the lens, for IRS/ITS-aided antennas, the reconfigurablility of the IRS/ITS facilitates scaling up the number of radiating passive elements without increasing the number of costly and bulky active antennas. We show that the constraints that the precoders for IRS/ITS-aided antennas have to meet differ from those of conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design two efficient precoders and develop a power consumption model for IRS/ITS-aided antennas that takes into account the impacts of the IRS/ITS imperfections, namely the spillover loss, taper loss, aperture loss, and phase shifter loss. Our simulation results reveal that unlike conventional MIMO architectures, IRS/ITS-aided antennas are both highly energy efficient and fully scalable in terms of the number of transmitting antennas. Therefore, IRS/ITS-aided antennas are promising candidates for realizing the potential of mmWave massive MIMO communications in practice.

Connected through hyperedge HE-1 (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"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "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-2 (induced by source paper "on the impact of phase shifting designs on irs noma"):
  with members:
    - "irs based wireless jamming attacks when jammers can attack without power"
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "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"
    - "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"
    - "a simple design of irs noma transmission"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"

Connected through hyperedge HE-3 (induced by source paper "beyond max snr joint encoding for reconfigurable intelligent surfaces"):
  with members:
    - "intelligent reflecting surface versus decode and forward how large surfaces are needed to beat relaying"
    - "intelligent reflecting surface with discrete phase shifts channel estimation and passive beamforming"
    - "channel estimation for wireless communication systems assisted by large intelligent surfaces"
    - "reflection probability in wireless networks with metasurface coated environmental objects an approach based on random spatial processes"
    - "capacity characterization for intelligent reflecting surface aided mimo communication"
    - "channel capacity optimization using reconfigurable intelligent surfaces in indoor mmwave environments"
    - "intelligent reflecting surface irs enabled covert communications in wireless networks"
    - "secure transmission strategy for intelligent reflecting surface enhanced wireless system"

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)