human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "hybrid beamforming for millimeter wave systems using the mmse criterion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "deep learning based channel estimation for beamspace mmwave massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning coordinated beamforming for highly mobile millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning in physical layer communications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "multi user precoding and channel estimation for hybrid millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "power of deep learning for channel estimation and signal detection in ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning for massive mimo csi feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a hybrid model based and data driven approach to spectrum sharing in mmwave cellular networks")

Question: Please tell me which class the source paper 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)