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: "blind one bit compressive sampling"
  Abstract: Abstract : The problem of 1-bit compressive sampling is addressed in this paper. We introduce an optimization model for reconstruction of sparse signals from 1-bit measurements. The model targets a solution that has the least l0-norm among all signals satisfying consistency constraints stemming from the 1-bit measurements. An algorithm for solving the model is developed. Convergence analysis of the algorithm is presented. Our approach is to obtain a sequence of optimization problems by successively approximating the l0-norm and to solve resulting problems by exploiting the proximity operator. We examine the performance of our proposed algorithm and compare it with the binary iterative hard thresholding (BIHT) [11] a state-of-the-art algorithm for 1-bit compressive sampling reconstruction. Unlike the BIHT, our model and algorithm does not require prior knowledge on the sparsity of the signal. This makes our proposed work a promising practical approach for signal acquisition.

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"
    - "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"
    - "a fast iterative algorithm for recovery of sparse signals from one bit quantized measurements"
    - "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 "1 bit compressive sensing reformulation and rrsp based sign recovery theory"):
  with members:
    - "robust 1 bit compressive sensing via gradient support pursuit"
    - "rsp based analysis for sparsest and least ell_1 norm solutions to underdetermined linear systems"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "one bit compressed sensing with non gaussian measurements"
    - "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"
    - "on polar polytopes and the recovery of sparse representations"

Connected through hyperedge HE-3 (induced by source paper "1 bit compressive data gathering for wireless sensor networks"):
  with members:
    - "efficient data gathering in wireless sensor networks based on matrix completion and compressive sensing"
    - "a fast iterative algorithm for recovery of sparse signals from one bit quantized measurements"
    - "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)