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 "deep learning for massive mimo csi feedback"):
  Abstract: In frequency division duplex mode, the downlink channel state information (CSI) should be conveyed to the base station through feedback links so that the potential gains of a massive multiple-input multiple-output can be exhibited. However, the excessive feedback overhead remains a bottleneck in this regime. In this letter, we use beep learning technology to develop CsiNet, a novel CSI sensing and recovery network that learns to effectively use channel structure from training samples. In particular, CsiNet learns a transformation from CSI to a near-optimal number of representations (codewords) and an inverse transformation from codewords to CSI. Experiments demonstrate that CsiNet can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods. Even at excessively low compression regions where CS-based methods cannot work, CsiNet retains effective beamforming gain.

Includes members:
  Member "dr2 net deep residual reconstruction network for image compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "deep learning for wireless physical layer opportunities and challenges":
    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 based mimo communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "convolutional neural networks for non iterative reconstruction of compressively sensed images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "highly scalable image reconstruction using deep neural networks with bandpass filtering")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "from denoising to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")

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)