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 "double sparsity learning based channel and signal estimation in massive mimo with generalized spatial modulation"):
  Abstract: In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity massive MIMO model by considering the channel sparsity of the massive MIMO channel and the signal sparsity of GSM. Based on the double-sparsity model, we formulate a blind detection problem. To solve the blind detection problem, we develop message-passing based blind channel-and-signal estimation (BCSE) algorithm. The BCSE algorithm basically follows the affine sparse matrix factorization technique, but with critical modifications to handle the double-sparsity property of the model. We show that the BCSE algorithm significantly outperforms the existing blind and training-based algorithms, and is able to closely approach the genie bounds (with either known channel or known signal). In the BCSE algorithm, short pilots are employed to remove the phase and permutation ambiguities after sparse matrix factorization. To utilize the short pilots more efficiently, we further develop the semi-blind channel-and-signal estimation (SBCSE) algorithm to incorporate the estimation of the phase and permutation ambiguities into the iterative message-passing process. We show that the SBCSE algorithm substantially outperforms the counterpart algorithms including the BCSE algorithm in the short-pilot regime.

Includes members:
  Member "bilinear recovery using adaptive vector amp":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascaded channel estimation for large intelligent metasurface assisted massive mimo")
  Member "complete dictionary recovery over the sphere i overview and the geometric picture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "parametric bilinear generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "generalized spatial modulation in large scale multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatial coded modulation")
  Member "bilinear generalized approximate message passing part i derivation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "optimal design of energy efficient multi user mimo systems is massive mimo the answer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rate splitting multiple access for multi antenna downlink communication systems spectral and energy efficiency tradeoff")

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)