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: "on convergence of approximate message passing"
  Abstract: Approximate message passing is an iterative algorithm for compressed sensing and related applications. A solid theory about the performance and convergence of the algorithm exists for measurement matrices having iid entries of zero mean. However, it was observed by several authors that for more general matrices the algorithm often encounters convergence problems. In this paper we identify the reason of the non-convergence for measurement matrices with iid entries and non-zero mean in the context of Bayes optimal inference. Finally we demonstrate numerically that when the iterative update is changed from parallel to sequential the convergence is restored.

Connected through hyperedge HE-1 (induced by source paper "inference for generalized linear models via alternating directions and bethe free energy minimization"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "bilinear generalized approximate message passing part i derivation"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"

Connected through hyperedge HE-2 (induced by source paper "blind sensor calibration using approximate message passing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "a factor graph approach to joint ofdm channel estimation and decoding in impulsive noise environments"
    - "blind calibration in compressed sensing using message passing algorithms"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"

Connected through hyperedge HE-3 (induced by source paper "approximate message passing with unitary transformation"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "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"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-4 (induced by source paper "adaptive damping and mean removal for the generalized approximate message passing algorithm"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "message passing de quantization with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-5 (induced by source paper "on the convergence of approximate message passing with arbitrary matrices"):
  with members:
    - "learning and free energies for vector approximate message passing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "variational free energies for compressed sensing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "generalized approximate message passing for cosparse analysis compressive sensing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "sparsity averaging for compressive imaging"
    - "fast and efficient compressive sensing using structurally random matrices"

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)