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: "on communication over unknown sparse frequency selective block fading channels"
  Abstract: This paper considers the problem of reliable communication over discrete-time channels whose impulse responses have length $L$ and exactly $S\leq L$ non-zero coefficients, and whose support and coefficients remain fixed over blocks of $N>L$ channel uses but change independently from block to block. Here, it is assumed that the channel's support and coefficient realizations are both unknown, although their statistics are known. Assuming Gaussian non-zero-coefficients and noise, and focusing on the high-SNR regime, it is first shown that the ergodic noncoherent channel capacity has pre-log factor $1-\frac{S}{N}$ for any $L$. It is then shown that, to communicate with arbitrarily small error probability at rates in accordance with the capacity pre-log factor, it suffices to use pilot-aided orthogonal frequency-division multiplexing (OFDM) with $S$ pilots per fading block, in conjunction with an appropriate noncoherent decoder. Since the achievability result is proven using a noncoherent decoder whose complexity grows exponentially in the number of fading blocks $K$, a simpler decoder, based on $S+1$ pilots, is also proposed. Its $\epsilon$-achievable rate is shown to have pre-log factor equal to $1-\frac{S+1}{N}$ with the previously considered channel, while its achievable rate is shown to have pre-log factor $1-\frac{S+1}{N}$ when the support of the block-fading channel remains fixed over time.

Connected through hyperedge HE-1 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse channel estimation for massive mimo with 1 bit feedback per dimension"

Connected through hyperedge HE-2 (induced by source paper "sparse multipath channel estimation and decoding for broadband vector ofdm systems"):
  with members:
    - "sparsity aware sphere decoding algorithms and complexity analysis"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-3 (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"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (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"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"
    - "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-5 (induced by source paper "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"):
  with members:
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"
    - "expectation maximization as message passing"

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)