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: "on sparse representation in fourier and local bases"
  Abstract: We consider the classical problem of finding the sparse representation of a signal in a pair of bases. When both bases are orthogonal, it is known that the sparse representation is unique when the sparsity $K$ of the signal satisfies $K<1/\mu(D)$, where $\mu(D)$ is the mutual coherence of the dictionary. Furthermore, the sparse representation can be obtained in polynomial time by Basis Pursuit (BP), when $K<0.91/\mu(D)$. Therefore, there is a gap between the unicity condition and the one required to use the polynomial-complexity BP formulation. For the case of general dictionaries, it is also well known that finding the sparse representation under the only constraint of unicity is NP-hard. #R##N#In this paper, we introduce, for the case of Fourier and canonical bases, a polynomial complexity algorithm that finds all the possible $K$-sparse representations of a signal under the weaker condition that $K<\sqrt{2} /\mu(D)$. Consequently, when $K<1/\mu(D)$, the proposed algorithm solves the unique sparse representation problem for this structured dictionary in polynomial time. We further show that the same method can be extended to many other pairs of bases, one of which must have local atoms. Examples include the union of Fourier and local Fourier bases, the union of discrete cosine transform and canonical bases, and the union of random Gaussian and canonical bases.

Connected through hyperedge HE-1 (induced by source paper "a tree based dictionary learning framework"):
  with members:
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "a region based easy path wavelet transform for sparse image representation"
    - "on the computational intractability of exact and approximate dictionary learning"
    - "the haar wavelet transform of a dendrogram"

Connected through hyperedge HE-2 (induced by source paper "sampling and reconstruction of sparse signals on circulant graphs an introduction to graph fri"):
  with members:
    - "splines and wavelets on circulant graphs"
    - "sampling of graph signals with successive local aggregations"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"
    - "estimation of laplacian spectra of direct and strong product graphs"
    - "signal recovery on graphs variation minimization"
    - "active semi supervised learning using sampling theory for graph signals"
    - "a multiscale pyramid transform for graph signals"

Connected through hyperedge HE-3 (induced by source paper "on probability of support recovery for orthogonal matching pursuit using mutual coherence"):
  with members:
    - "exact recovery conditions for sparse representations with partial support information"

Connected through hyperedge HE-4 (induced by source paper "sparse representation in fourier and local bases using prosparse a probabilistic analysis"):
  with members:
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "recovery of sparsely corrupted signals"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "sparse approximation list decoding and uncertainty principles"):
  with members:
    - "every list decodable code for high noise has abundant near optimal rate puncturings"
    - "on the list decodability of random linear codes with large error rates"
    - "combinatorial limitations of average radius list decoding"
    - "the sparsity gap uncertainty principles proportional to dimension"

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)