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: "subsampling at information theoretically optimal rates"
  Abstract: We study the problem of sampling a random signal with sparse support in frequency domain. Shannon famously considered a scheme that instantaneously samples the signal at equispaced times. He proved that the signal can be reconstructed as long as the sampling rate exceeds twice the bandwidth (Nyquist rate). Cand\`es, Romberg, Tao introduced a scheme that acquires instantaneous samples of the signal at random times. They proved that the signal can be uniquely and efficiently reconstructed, provided the sampling rate exceeds the frequency support of the signal, times logarithmic factors. #R##N#In this paper we consider a probabilistic model for the signal, and a sampling scheme inspired by the idea of spatial coupling in coding theory. Namely, we propose to acquire non-instantaneous samples at random times. Mathematically, this is implemented by acquiring a small random subset of Gabor coefficients. We show empirically that this scheme achieves correct reconstruction as soon as the sampling rate exceeds the frequency support of the signal, thus reaching the information theoretic limit.

Connected through hyperedge HE-1 (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-2 (induced by source paper "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"):
  with members:
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "s amp for non linear observation models"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "support recovery with sparsely sampled free random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-3 (induced by source paper "scampi a robust approximate message passing framework for compressive imaging"):
  with members:
    - "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"
    - "variational free energies for compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "properties of spatial coupling in compressed sensing"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"

Connected through hyperedge HE-5 (induced by source paper "on sparse vector recovery performance in structurally orthogonal matrices via lasso"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "multi processor approximate message passing using lossy compression"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"

Connected through hyperedge HE-6 (induced by source paper "two part reconstruction with noisy sudocodes"):
  with members:
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "variational bayesian algorithm for quantized compressed sensing"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"
    - "performance regions in compressed sensing from noisy measurements"
    - "two part reconstruction in compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-7 (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"
    - "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"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-8 (induced by source paper "robust error correction for real valued signals via message passing decoding and spatial coupling"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "compressed sensing with sparse structured matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the effect of spatial coupling on compressive sensing"

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)