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: "weighted superimposed codes and constrained integer compressed sensing"
  Abstract: We introduce a new family of codes, termed weighted superimposed codes (WSCs). This family generalizes the class of Euclidean superimposed codes (ESCs), used in multiuser identification systems. WSCs allow for discriminating all bounded, integer-valued linear combinations of real-valued codewords that satisfy prescribed norm and non-negativity constraints. By design, WSCs are inherently noise tolerant. Therefore, these codes can be seen as special instances of robust compressed sensing schemes. The main results of the paper are lower and upper bounds on the largest achievable code rates of several classes of WSCs. These bounds suggest that with the codeword and weighting vector constraints at hand, one can improve the code rates achievable by standard compressive sensing.

Connected through hyperedge HE-1 (induced by source paper "binary compressive sensing via analog fountain coding"):
  with members:
    - "near capacity adaptive analog fountain codes for wireless channels"
    - "wireless compressive sensing for energy harvesting sensor nodes"
    - "measure what should be measured progress and challenges in compressive sensing"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "restricted isometry property of random subdictionaries"):
  with members:
    - "random subdictionaries and coherence conditions for sparse signal recovery"
    - "lower bounds for sparse recovery"
    - "reed muller sensing matrices and the lasso"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "explicit non adaptive combinatorial group testing schemes"

Connected through hyperedge HE-3 (induced by source paper "poisson group testing a probabilistic model for boolean compressed sensing"):
  with members:
    - "limits on support recovery with probabilistic models an information theoretic framework"
    - "group testing with prior statistics"
    - "lectures on designing screening experiments"
    - "the capacity of non identical adaptive group testing"
    - "group testing algorithms bounds and simulations"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"

Connected through hyperedge HE-4 (induced by source paper "semiquantitative group testing"):
  with members:
    - "lectures on designing screening experiments"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "boolean compressed sensing and noisy group testing"
    - "semi quantitative group testing"
    - "non adaptive probabilistic group testing with noisy measurements near optimal bounds with efficient algorithms"
    - "improved constructions for non adaptive threshold group testing"
    - "note on noisy group testing asymptotic bounds and belief propagation reconstruction"
    - "fast min sum algorithms for decoding of ldpc over gf q"

Connected through hyperedge HE-5 (induced by source paper "code construction and decoding algorithms for semi quantitative group testing with nonuniform thresholds"):
  with members:
    - "non adaptive group testing with inhibitors"
    - "lectures on designing screening experiments"
    - "semiquantitative group testing"
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "semi quantitative group testing"

Connected through hyperedge HE-6 (induced by source paper "a problem dependent analysis of socp algorithms in noisy compressed sensing"):
  with members:
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "block length dependent thresholds in block sparse compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "a performance analysis framework for socp algorithms in noisy compressed sensing"):
  with members:
    - "model based compressive sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-8 (induced by source paper "random subdictionaries and coherence conditions for sparse signal recovery"):
  with members:
    - "lower bounds for sparse recovery"
    - "reed muller sensing matrices and the lasso"
    - "statistical rip and semi circle distribution of incoherent dictionaries"

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)