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: "a fast iterative algorithm for recovery of sparse signals from one bit quantized measurements"
  Abstract: This paper considers the problem of reconstructing sparse or compressible signals from one-bit quantized measurements. We study a new method that uses a log-sum penalty function, also referred to as the Gaussian entropy, for sparse signal recovery. Also, in the proposed method, sigmoid functions are introduced to quantify the consistency between the acquired one-bit quantized data and the reconstructed measurements. A fast iterative algorithm is developed by iteratively minimizing a convex surrogate function that bounds the original objective function, which leads to an iterative reweighted process that alternates between estimating the sparse signal and refining the weights of the surrogate function. Connections between the proposed algorithm and other existing methods are discussed. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

Connected through hyperedge HE-1 (induced by source paper "robust binary fused compressive sensing using adaptive outlier pursuit"):
  with members:
    - "binary fused compressive sensing 1 bit compressive sensing meets group sparsity"
    - "blind one bit compressive sampling"
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "statistical mechanics approach to 1 bit compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "bits from photons oversampled image acquisition using binary poisson statistics"
    - "regime change bit depth versus measurement rate in compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "binary fused compressive sensing 1 bit compressive sensing meets group sparsity"):
  with members:
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "bits from photons oversampled image acquisition using binary poisson statistics"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "1 bit compressive data gathering for wireless sensor networks"):
  with members:
    - "blind one bit compressive sampling"
    - "efficient data gathering in wireless sensor networks based on matrix completion and compressive sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "distributed compressive sensing"
    - "exact matrix completion via convex optimization"

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)