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: "beamforming design for large scale antenna arrays using deep learning"
  Abstract: Beamforming (BF) design for large-scale antenna arrays with limited radio frequency chains and the phase-shifter-based analog BF architecture, has been recognized as a key issue in millimeter wave communication systems. It becomes more challenging with imperfect channel state information (CSI). In this letter, we propose a deep learning based BF design approach and develop a BF neural network (BFNN) which can be trained to learn how to optimize the beamformer for maximizing the spectral efficiency with hardware limitation and imperfect CSI. Simulation results show that the proposed BFNN achieves significant performance improvement and strong robustness to imperfect CSI over the traditional BF algorithms.

Connected through hyperedge HE-1 (induced by source paper "reconfigurable intelligent surface assisted multiuser miso systems exploiting deep reinforcement learning"):
  with members:
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "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-2 (induced by source paper "unsupervised learning for passive beamforming"):
  with members:
    - "indoor signal focusing with deep learning designed reconfigurable intelligent surfaces"
    - "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"

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)