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: "statistical mechanics approach to 1 bit compressed sensing"
  Abstract: Compressed sensing is a framework that makes it possible to recover an N-dimensional sparse vector x∈RN from its linear transformation y∈RM of lower dimensionality M < N. A scheme further reducing the data size of the compressed expression by using only the sign of each entry of y to recover x was recently proposed. This is often termed 1-bit compressed sensing. Here, we analyze the typical performance of an l1-norm-based signal recovery scheme for 1-bit compressed sensing using statistical mechanics methods. We show that the signal recovery performance predicted by the replica method under the replica symmetric ansatz, which turns out to be locally unstable for modes breaking the replica symmetry, is in good consistency with experimental results of an approximate recovery algorithm developed earlier. This suggests that the l1-based recovery problem typically has many local optima of a similar recovery accuracy, which can be achieved by the approximate algorithm. We also develop another approximate recovery algorithm inspired by the cavity method. Numerical experiments show that when the density of nonzero entries in the original signal is relatively large the new algorithm offers better performance than the abovementioned scheme and does so with a lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "bayesian online compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sequential compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "statistical physics of linear and bilinear inference problems"):
  with members:
    - "binary linear classification and feature selection via generalized approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "critical behavior and universality classes for an algorithmic phase transition in sparse reconstruction"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "sparse estimation with the swept approximated message passing algorithm"):
  with members:
    - "expectation maximization gaussian mixture approximate message passing"
    - "message passing de quantization with applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

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)