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: "proof of threshold saturation for spatially coupled sparse superposition codes"
  Abstract: Recently, a new class of codes, called sparse superposition or sparse regression codes, has been proposed for communication over the AWGN channel. It has been proven that they achieve capacity using power allocation and various forms of iterative decoding. Empirical evidence has also strongly suggested that the codes achieve capacity when spatial coupling and approximate message passing decoding are used, without need of power allocation. In this note we prove that state evolution (which tracks message passing) indeed saturates the potential threshold of the underlying code ensemble, which approaches in a proper limit the optimal threshold. Our proof uses ideas developed in the theory of low-density parity-check codes and compressive sensing.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "finite sample analysis of approximate message passing algorithms"
    - "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"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (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-3 (induced by source paper "the error probability of sparse superposition codes with approximate message passing decoding"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "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"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "phase transitions in sparse pca"
    - "information theoretically optimal sparse pca"
    - "replica analysis and approximate message passing decoder for superposition codes"

Connected through hyperedge HE-4 (induced by source paper "a compressed sensing approach for distribution matching"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "generalized approximate message passing decoder for universal sparse superposition codes"
    - "comparison of geometric and probabilistic shaping with application to atsc 3 0"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "constant composition distribution matching"

Connected through hyperedge HE-5 (induced by source paper "spatially coupled sparse regression codes design and state evolution analysis"):
  with members:
    - "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"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "spatially coupled ldpc codes constructed from protographs"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "threshold saturation for spatially coupled ldpc and ldgm codes on bms channels"

Connected through hyperedge HE-6 (induced by source paper "universal sparse superposition codes with spatial coupling and gamp decoding"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "a compressed sensing approach for distribution matching"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "state evolution for approximate message passing with non separable functions"
    - "generalized approximate message passing decoder for universal sparse superposition codes"

Connected through hyperedge HE-7 (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"
    - "compressive phase retrieval via generalized approximate message passing"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"

Connected through hyperedge HE-8 (induced by source paper "mutual information and optimality of approximate message passing in random linear estimation"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "the mutual information in random linear estimation"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"

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)