human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "1 bit compressive sensing reformulation and rrsp based sign recovery theory"):
  Abstract: Recently, the 1-bit compressive sensing (1-bit CS) has been studied in the field of sparse signal recovery. Since the amplitude information of sparse signals in 1-bit CS is not available, it is often the support or the sign of a signal that can be exactly recovered with a decoding method. In this paper, we first show that a necessary assumption (that has been overlooked in the literature) should be made for some existing theories and discussions for 1-bit CS. Without such an assumption, the found solution by some existing decoding algorithms might be inconsistent with 1-bit measurements. This motivates us to pursue a new direction to develop uniform and nonuniform recovery theories for 1-bit CS with a new decoding method which always generates a solution consistent with 1-bit measurements. We focus on an extreme case of 1-bit CS, in which the measurements capture only the sign of the product of a sensing matrix and a signal. We show that the 1-bit CS model can be reformulated equivalently as an $\ell_0$-minimization problem with linear constraints. This reformulation naturally leads to a new linear-program-based decoding method, referred to as the 1-bit basis pursuit, which is remarkably different from existing formulations. It turns out that the uniqueness condition for the solution of the 1-bit basis pursuit yields the so-called restricted range space property (RRSP) of the transposed sensing matrix. This concept provides a basis to develop sign recovery conditions for sparse signals through 1-bit measurements. We prove that if the sign of a sparse signal can be exactly recovered from 1-bit measurements with 1-bit basis pursuit, then the sensing matrix must admit a certain RRSP, and that if the sensing matrix admits a slightly enhanced RRSP, then the sign of a $k$-sparse signal can be exactly recovered with 1-bit basis pursuit.

Includes members:
  Member "blind one bit compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust binary fused compressive sensing using adaptive outlier pursuit")
  Member "robust 1 bit compressive sensing via gradient support pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex penalties with analytical solutions for one bit compressive sensing")
  Member "rsp based analysis for sparsest and least ell_1 norm solutions to underdetermined linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uniqueness conditions for a class of l0 minimization problems")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "one bit compressed sensing with non gaussian measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "regime change bit depth versus measurement rate in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quantized compressed sensing by rectified linear units")
  Member "one bit compressed sensing by linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

Question: Please tell me which class the source paper 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)