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: "replica analysis and approximate message passing decoder for superposition codes"
  Abstract: Superposition codes are efficient for the Additive White Gaussian Noise channel. We provide here a replica analysis of the performances of these codes for large signals. We also consider a Bayesian Approximate Message Passing decoder based on a belief-propagation approach, and discuss its performance using the density evolution technic. Our main findings are 1) for the sizes we can access, the message-passing decoder outperforms other decoders studied in the literature 2) its performance is limited by a sharp phase transition and 3) while these codes reach capacity as $B$ (a crucial parameter in the code) increases, the performance of the message passing decoder worsen as the phase transition goes to lower rates.

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 "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"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "phase transitions in sparse pca"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-3 (induced by source paper "finite sample analysis of approximate message passing algorithms"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-4 (induced by source paper "an improved analysis of least squares superposition codes with bernoulli dictionary"):
  with members:
    - "sparse regression codes"
    - "finite sample analysis of approximate message passing algorithms"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "techniques for improving the finite length performance of 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"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "generalized approximate message passing for estimation with random linear mixing"

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"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "compressive phase retrieval via generalized approximate message passing"

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"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"

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)