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: "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
  Abstract: We propose a factor-graph-based approach to joint channel-estimation-and-decoding (JCED) of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to existing designs, ours is capable of exploiting not only sparsity in sampled channel taps but also clustering among the large taps, behaviors which are known to manifest at larger communication bandwidths. In order to exploit these channel-tap structures, we adopt a two-state Gaussian mixture prior in conjunction with a Markov model on the hidden state. For loopy belief propagation, we exploit a “generalized approximate message passing” (GAMP) algorithm recently developed in the context of compressed sensing, and show that it can be successfully coupled with soft-input soft-output decoding, as well as hidden Markov inference, through the standard sum-product framework. For N subcarriers and any channel length L<;N, the resulting JCED-GAMP scheme has a computational complexity of only O(N log2 N +N|S|), where |S| is the constellation size. Numerical experiments using IEEE 802.15.4a channels show that our scheme yields BER performance within 1 dB of the known-channel bound and 3-4 dB better than soft equalization based on LMMSE and LASSO.

Connected through hyperedge HE-1 (induced by source paper "capacity optimality of amp in coded systems"):
  with members:
    - "capacity achieving mimo noma iterative lmmse detection"
    - "a unified framework of state evolution for message passing algorithms"
    - "optimal data detection in large mimo"
    - "near gaussian multiple access channel capacity detection and decoding"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "optimal data detection in large mimo"):
  with members:
    - "on the performance of mismatched data detection in large mimo systems"
    - "precise error analysis of regularized m estimators in high dimensions"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "compressive phase retrieval via generalized approximate message passing"
    - "does ell_p minimization outperform ell_1 minimization"
    - "large scale mimo detection for 3gpp lte algorithms and fpga implementations"
    - "massive mimo for next generation wireless systems"
    - "what will 5g be"

Connected through hyperedge HE-4 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "capacity achieving sparse superposition codes via approximate message passing decoding"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "construction of capacity achieving lattice codes polar lattices"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"

Connected through hyperedge HE-6 (induced by source paper "low complexity message passing based massive mimo channel estimation by exploiting unknown sparse common support with dirichlet process"):
  with members:
    - "massive mimo for next generation wireless systems"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "estimation of sparse mimo channels with common support"

Connected through hyperedge HE-7 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

Connected through hyperedge HE-8 (induced by source paper "learning and free energies for vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "binary linear classification and feature selection via generalized approximate 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)