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: "sparse reconstruction via the reed muller sieve"
  Abstract: This paper introduces the Reed Muller Sieve, a deterministic measurement matrix for compressed sensing. The columns of this matrix are obtained by exponentiating codewords in the quaternary second order Reed Muller code of length $N$. For $k=O(N)$, the Reed Muller Sieve improves upon prior methods for identifying the support of a $k$-sparse vector by removing the requirement that the signal entries be independent. The Sieve also enables local detection; an algorithm is presented with complexity $N^2 \log N$ that detects the presence or absence of a signal at any given position in the data domain without explicitly reconstructing the entire signal. Reconstruction is shown to be resilient to noise in both the measurement and data domains; the $\ell_2 / \ell_2$ error bounds derived in this paper are tighter than the $\ell_2 / \ell_1$ bounds arising from random ensembles and the $\ell_1 /\ell_1$ bounds arising from expander-based ensembles.

Connected through hyperedge HE-1 (induced by source paper "near optimal binary compressed sensing matrix"):
  with members:
    - "ldpc codes for compressed sensing"
    - "sparse vector distributions and recovery from compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"

Connected through hyperedge HE-2 (induced by source paper "structured sublinear compressive sensing via belief propagation"):
  with members:
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "bayesian compressive sensing via belief propagation"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "encoding for the blackwell channel with reinforced belief propagation"

Connected through hyperedge HE-3 (induced by source paper "reed muller sensing matrices and the lasso"):
  with members:
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-4 (induced by source paper "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"):
  with members:
    - "compressive sensing using low density frames"
    - "incoherent dictionaries and the statistical restricted isometry property"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)