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 "compressed csi feedback with learned measurement matrix for mmwave massive mimo"):
  Abstract: A major challenge to implement compressed sensing method for channel state information (CSI) acquisition lies in the design of a well-performed measurement matrix to subsample sparse channel vectors. The widely adopted randomized measurement matrices drawn from Gaussian or Bernoulli distribution may not be optimal. To tackle this problem, we propose a fully data-driven approach to learn a measurement matrix for beamspace channel compression, and this method trains a mathematically interpretable autoencoder constructed according to the iterative solution of sparse recovery. The learned measurement matrix can achieve near perfect CSI recovery with fewer required measurements, thus the feedback overhead can be substantially reduced.

Includes members:
  Member "a fast noniterative algorithm for compressive sensing using binary measurement matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse graph codes for non adaptive quantitative group testing")
  Member "compressive sensing techniques for next generation wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "an overview of signal processing techniques for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "compressed sensing for wireless communications useful tips and tricks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "broadband channel estimation for intelligent reflecting surface aided mmwave massive mimo systems")
  Member "compressed sensing based multi user millimeter wave systems how many measurements are needed":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")

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)