human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"
  Abstract: We consider spectrally-efficient communication over a Rayleigh N-block-fading channel with a K- sparse L-length discrete-time impulse response (for 0<K<L<N), where neither the transmitter nor receiver know the channel's coefficients nor its support. Since the high-SNR ergodic capacity of this channel has been shown to obey C(SNR) = (1-K/N)log2(SNR)+O(1), any pilot-aided scheme that sacrifices more than K dimensions per fading block to pilots will be spectrally inefficient. This causes concern about the conventional "compressed channel sensing" approach, which uses O(K polylog L) pilots. In this paper, we demonstrate that practical spectrally-efficient communication is indeed possible. For this, we propose a novel belief-propagation-based reception scheme to use with a standard bit- interleaved coded orthogonal frequency division multiplexing (OFDM) transmitter. In particular, we leverage the "relaxed belief propagation" methodology, which allows us to perform joint sparse-channel estimation and data decoding with only O(LN) complexity. Empirical results show that our receiver achieves the desired capacity pre-log factor of 1 - K/N and performs near genie-aided bounds at both low and high SNR.

Connected through hyperedge HE-1 (induced by source paper "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "recovery of sparsely corrupted signals"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "on communication over unknown sparse frequency selective block fading channels"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "a message passing receiver for bicm ofdm over unknown clustered sparse channels"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "on communication over unknown sparse frequency selective block fading channels"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "expectation maximization as message passing"

Connected through hyperedge HE-3 (induced by source paper "on communication over unknown sparse frequency selective block fading channels"):
  with members:
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Question: Please tell me which class the center node 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)