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 "super resolution blind channel and signal estimation for massive mimo with one dimensional antenna array"):
  Abstract: In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method.

Includes members:
  Member "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "phase transitions and sample complexity in bayes optimal matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "dictionary learning based sparse channel representation and estimation for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "statistical beamforming for fdd downlink massive mimo via spatial information extraction and beam selection")
  Member "fundamental limits of training based multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structured turbo compressed sensing for downlink massive mimo ofdm channel estimation")
  Member "turbo compressed sensing with partial dft sensing matrix":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting")
  Member "channel estimation in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pilot power allocation through user grouping in multi cell massive mimo systems")
  Member "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multiple antenna technologies for beyond 5g")

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)