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 the presence of 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streaming, where the signal-independent and/or bounded noise models used in the compressed sensing literature are no longer applicable.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 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. The geometry of the expander graphs makes them provably superior to random dense sensing matrices, such as Gaussian or partial Fourier ensembles, for the Poisson noise model. We support our results with experimental demonstrations.

Connected through hyperedge HE-1 (induced by source paper "sparsification of matrices and compressed sensing"):
  with members:
    - "deterministic constructions of binary measurement matrices from finite geometry"
    - "near optimal binary compressed sensing matrix"
    - "ldpc codes for compressed sensing"
    - "sparsity lower bounds for dimensionality reducing maps"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "vanishingly sparse matrices and expander graphs with application to compressed sensing"
    - "restricted isometries for partial random circulant matrices"
    - "approximate sparse recovery optimizing time and measurements"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing performance bounds under poisson noise"):
  with members:
    - "sparse recovery of positive signals with minimal expansion"
    - "weighted superimposed codes and constrained integer compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "fishing in poisson streams focusing on the whales ignoring the minnows"):
  with members:
    - "sparse recovery of positive signals with minimal expansion"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)