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: "the sparsity gap uncertainty principles proportional to dimension"
  Abstract: In an incoherent dictionary, most signals that admit a sparse representation admit a unique sparse representation. In other words, there is no way to express the signal without using strictly more atoms. This work demonstrates that sparse signals typically enjoy a higher privilege: each nonoptimal representation of the signal requires far more atoms than the sparsest representation—unless it contains many of the same atoms as the sparsest representation. One impact of this finding is to confer a certain degree of legitimacy on the particular atoms that appear in a sparse representation. This result can also be viewed as an uncertainty principle for random sparse signals over an incoherent dictionary.

Connected through hyperedge HE-1 (induced by source paper "discrete uncertainty principles and sparse signal processing"):
  with members:
    - "recovery of signals with low density"
    - "robust width a characterization of uniformly stable and robust compressed sensing"
    - "convexity in source separation models geometry and algorithms"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "estimating unknown sparsity in compressed sensing"
    - "sharp recovery bounds for convex demixing with applications"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "sparse approximation list decoding and uncertainty principles"):
  with members:
    - "on sparse representation in fourier and local bases"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deterministic compressed sensing matrices from multiplicative character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "reed muller sensing matrices and the lasso"
    - "deterministic compressed sensing matrices from additive character sequences"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"

Connected through hyperedge HE-4 (induced by source paper "reed muller sensing matrices and the lasso"):
  with members:
    - "sparse reconstruction via the reed muller sieve"
    - "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-5 (induced by source paper "deterministic compressed sensing matrices from additive character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "reed muller sensing matrices and the lasso"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"

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)