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: "efficient and robust compressed sensing using high quality expander graphs"
  Abstract: Expander graphs have been recently proposed to construct efficient compressed sensing algorithms. In particular, it has been shown that any $n$-dimensional vector that is $k$-sparse (with $k\ll n$) can be fully recovered using $O(k\log\frac{n}{k})$ measurements and only $O(k\log n)$ simple recovery iterations. In this paper we improve upon this result by considering expander graphs with expansion coefficient beyond 3/4 and show that, with the same number of measurements, only $O(k)$ recovery iterations are required, which is a significant improvement when $n$ is large. In fact, full recovery can be accomplished by at most $2k$ very simple iterations. The number of iterations can be made arbitrarily close to $k$, and the recovery algorithm can be implemented very efficiently using a simple binary search tree. We also show that by tolerating a small penalty on the number of measurements, and not on the number of recovery iterations, one can use the efficient construction of a family of expander graphs to come up with explicit measurement matrices for this method. We compare our result with other recently developed expander-graph-based methods and argue that it compares favorably both in terms of the number of required measurements and in terms of the recovery time complexity. Finally we will show how our analysis extends to give a robust algorithm that finds the position and sign of the $k$ significant elements of an almost $k$-sparse signal and then, using very simple optimization techniques, finds in sublinear time a $k$-sparse signal which approximates the original signal with very high precision.

Connected through hyperedge HE-1 (induced by source paper "vanishingly sparse matrices and expander graphs with application to compressed sensing"):
  with members:
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "phase transitions for greedy sparse approximation algorithms"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-2 (induced by source paper "message passing algorithms for compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "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"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "noise resilient group testing limitations and constructions"):
  with members:
    - "folded codes from function field towers and improved optimal rate list decoding"
    - "linear algebraic list decoding of folded reed solomon codes"
    - "subspace evasive sets"
    - "applications of derandomization theory in coding"
    - "improved combinatorial group testing algorithms for real world problem sizes"
    - "explicit codes achieving list decoding capacity error correction with optimal redundancy"

Connected through hyperedge HE-4 (induced by source paper "bit precision analysis for compressed sensing"):
  with members:
    - "quantized compressive sensing"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-5 (induced by source paper "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"):
  with members:
    - "incoherent dictionaries and the statistical restricted isometry property"
    - "the finite harmonic oscillator and its associated sequences"
    - "deterministic designs with deterministic guarantees toeplitz compressed sensing matrices sequence designs and system identification"
    - "on some deterministic dictionaries supporting sparsity"
    - "the finite harmonic oscillator and its applications to sequences communication and radar"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"
    - "on the diagonalization of the discrete fourier transform"

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)