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 "coded compressive sensing a compute and recover approach"):
  Abstract: In this paper, we propose \textit{coded compressive sensing} that recovers an $n$-dimensional integer sparse signal vector from a noisy and quantized measurement vector whose dimension $m$ is far-fewer than $n$. The core idea of coded compressive sensing is to construct a linear sensing matrix whose columns consist of lattice codes. We present a two-stage decoding method named \textit{compute-and-recover} to detect the sparse signal from the noisy and quantized measurements. In the first stage, we transform such measurements into noiseless finite-field measurements using the linearity of lattice codewords. In the second stage, syndrome decoding is applied over the finite-field to reconstruct the sparse signal vector. A sufficient condition of a perfect recovery is derived. Our theoretical result demonstrates an interplay among the quantization level $p$, the sparsity level $k$, the signal dimension $n$, and the number of measurements $m$ for the perfect recovery. Considering 1-bit compressive sensing as a special case, we show that the proposed algorithm empirically outperforms an existing greedy recovery algorithm.

Includes members:
  Member "map support detection for greedy sparse signal recovery algorithms in compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian approach with extended support estimation for sparse regression")
  Member "on finite alphabet compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "map support detection for greedy sparse signal recovery algorithms in compressive sensing")
  Member "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "compute and forward strategies for cooperative distributed antenna systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "private information retrieval over gaussian mac")
  Member "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "serial quantization for sparse time sequences")
  Member "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reed muller codes theory and algorithms")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "private information retrieval over gaussian mac")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)