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: "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
  Abstract: For the additive white Gaussian noise channel with average codeword power constraint, new coding methods are devised in which the codewords are sparse superpositions, that is, linear combinations of subsets of vectors from a given design, with the possible messages indexed by the choice of subset. Decoding is by least squares, tailored to the assumed form of linear combination. Communication is shown to be reliable with error probability exponentially small for all rates up to the Shannon capacity.

Connected through hyperedge HE-1 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "approximate message passing decoder and capacity achieving sparse superposition codes"):
  with members:
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "the mutual information in random linear estimation"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "proof of threshold saturation for spatially coupled sparse superposition codes"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "properties of spatial coupling in compressed sensing"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"
    - "a simple proof of maxwell saturation for coupled scalar recursions"
    - "replica analysis and approximate message passing decoder for superposition codes"

Connected through hyperedge HE-4 (induced by source paper "replica analysis and approximate message passing decoder for superposition codes"):
  with members:
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "subsampling at information theoretically optimal rates"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-5 (induced by source paper "lossy compression via sparse linear regression computationally efficient encoding and decoding"):
  with members:
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "sparse regression codes for multi terminal source and channel coding"
    - "fast sparse superposition codes have exponentially small error probability for r l c"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the dispersion of lossy source coding"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-6 (induced by source paper "lossy compression via sparse linear regression performance under minimum distance encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "rate distortion via markov chain monte carlo"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-7 (induced by source paper "sparse regression codes for multi terminal source and channel coding"):
  with members:
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "fast sparse superposition codes have exponentially small error probability for r l c"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless 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)