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: "properties of spatial coupling in compressed sensing"
  Abstract: In this paper we address a series of open questions about the construction of spatially coupled measurement matrices in compressed sensing. For hardware implementations one is forced to depart from the limiting regime of parameters in which the proofs of the so-called threshold saturation work. We investigate quantitatively the behavior under finite coupling range, the dependence on the shape of the coupling interaction, and optimization of the so-called seed to minimize distance from optimality. Our analysis explains some of the properties observed empirically in previous works and provides new insight on spatially coupled compressed sensing.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"
    - "a simple proof of threshold saturation for coupled scalar recursions"

Connected through hyperedge HE-3 (induced by source paper "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "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"
    - "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"
    - "a simple proof of threshold saturation for coupled scalar recursions"

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"
    - "replica analysis and approximate message passing decoder for superposition codes"
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "subsampling at information theoretically optimal rates"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "information theoretically optimal compressed sensing via spatial coupling and 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)