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: "performance bounds for expander based compressed sensing in poisson noise"
  Abstract: This paper provides performance bounds for compressed sensing in the presence of Poisson noise using expander graphs. The Poisson noise model is appropriate for a variety of applications, including low-light imaging and digital streaming, where the signal-independent and/or bounded noise models used in the compressed sensing literature are no longer applicable. In this paper, we develop a novel sensing paradigm based on expander graphs and propose a maximum a posteriori (MAP) algorithm for recovering sparse or compressible signals from Poisson observations. The geometry of the expander graphs and the positivity of the corresponding sensing matrices play a crucial role in establishing the bounds on the signal reconstruction error of the proposed algorithm. We support our results with experimental demonstrations of reconstructing average packet arrival rates and instantaneous packet counts at a router in a communication network, where the arrivals of packets in each flow follow a Poisson process.

Connected through hyperedge HE-1 (induced by source paper "poisson matrix recovery and completion"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "how well can we estimate a sparse vector"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "compressed sensing performance bounds under poisson noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "set an algorithm for consistent matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"

Connected through hyperedge HE-2 (induced by source paper "reconstruction error bounds for compressed sensing under poisson noise using the square root of the jensen shannon divergence"):
  with members:
    - "poisson matrix recovery and completion"
    - "the generalized lasso with non linear observations"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "stable restoration and separation of approximately sparse signals"
    - "learning exponential families in high dimensions strong convexity and sparsity"
    - "sparse recovery by non convex optimization instance optimality"

Connected through hyperedge HE-3 (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"
    - "subsampling at information theoretically optimal rates"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "one bit compressed sensing by linear programming"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic and ripless theory of compressed sensing"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"
    - "new bounds for restricted isometry constants"

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)