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: "hybrid approximate message passing with applications to structured sparsity"
  Abstract: Gaussian and quadratic approximations of message passing algorithms on graphs have attracted considerable recent attention due to their computational simplicity, analytic tractability, and wide applicability in optimization and statistical inference problems. This paper presents a systematic framework for incorporating such approximate message passing (AMP) methods in general graphical models. The key concept is a partition of dependencies of a general graphical model into strong and weak edges, with the weak edges representing interactions through aggregates of small, linearizable couplings of variables. AMP approximations based on the Central Limit Theorem can be readily applied to the weak edges and integrated with standard message passing updates on the strong edges. The resulting algorithm, which we call hybrid generalized approximate message passing (Hybrid-GAMP), can yield significantly simpler implementations of sum-product and max-sum loopy belief propagation. By varying the partition of strong and weak edges, a performance-complexity trade-off can be achieved. Structured sparsity problems are studied as an example of this general methodology where there is a natural partition of edges.

Connected through hyperedge HE-1 (induced by source paper "random access in c ran for user activity detection with limited capacity fronthaul"):
  with members:
    - "hybrid approximate message passing"
    - "q ary compressive sensing"
    - "compressive demodulation of mutually interfering signals"
    - "message passing de quantization with applications to compressed sensing"
    - "hybrid approximate message passing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "inferring sparsity compressed sensing using generalized restricted boltzmann machines"):
  with members:
    - "hybrid approximate message passing"
    - "reconnet non iterative reconstruction of images from compressively sensed random measurements"
    - "approximate message passing with restricted boltzmann machine priors"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "a survey of sparse representation algorithms and applications"
    - "a deep learning approach to structured signal recovery"
    - "hybrid approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-3 (induced by source paper "sparse multinomial logistic regression via approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "parameterless optimal approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "hybrid approximate message passing"

Connected through hyperedge HE-4 (induced by source paper "approximate message passing with restricted boltzmann machine priors"):
  with members:
    - "hybrid approximate message passing"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"
    - "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-5 (induced by source paper "signal estimation with additive error metrics in compressed sensing"):
  with members:
    - "hybrid approximate message passing"
    - "signal reconstruction in linear mixing systems with different error metrics"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "hybrid approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "signal reconstruction in linear mixing systems with different error metrics"):
  with members:
    - "hybrid approximate message passing"
    - "signal estimation with additive error metrics in compressed sensing"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "hybrid approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "estimation with random linear mixing belief propagation and compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "message passing de quantization with applications to compressed sensing"):
  with members:
    - "hybrid approximate message passing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "hybrid approximate message passing"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "universal rate efficient scalar quantization"

Connected through hyperedge HE-8 (induced by source paper "ranked sparse signal support detection"):
  with members:
    - "orthogonal matching pursuit a brownian motion analysis"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "shannon theoretic limits on noisy compressive sampling"

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)