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: "statistical mechanical analysis of compressed sensing utilizing correlated compression matrix"
  Abstract: We investigate a reconstruction limit of compressed sensing for a reconstruction scheme based on the L1-norm minimization utilizing a correlated compression matrix with a statistical mechanics method. We focus on the compression matrix modeled as the Kronecker-type random matrix studied in research on multi-input multi-output wireless communication systems. We found that strong one-dimensional correlations between expansion bases of original information slightly degrade reconstruction performance.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing performance analysis via replica method using bayesian framework"):
  with members:
    - "applications of large random matrices in communications engineering"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "vector precoding for gaussian mimo broadcast channels impact of replica symmetry breaking"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "analysis of sparse representations using bi orthogonal dictionaries"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "optimal incorporation of sparsity information by weighted l 1 optimization"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "a study of the universal threshold in the l 1 recovery by statistical mechanics"):
  with members:
    - "statistical mechanical assessment of a reconstruction limit of compressed sensing toward theoretical analysis of correlated signals"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

Connected through hyperedge HE-4 (induced by source paper "sparse representation of white gaussian noise with application to l0 norm decoding in noisy compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "statistical mechanical analysis of a typical reconstruction limit of compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"

Connected through hyperedge HE-5 (induced by source paper "statistical mechanical analysis of a typical reconstruction limit of compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "statistical mechanical analysis of the linear vector channel in digital communication"
    - "analysis of cdma systems that are characterized by eigenvalue spectrum"

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)