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: "uncertainty relations and sparse signal recovery for pairs of general signal sets"
  Abstract: We present an uncertainty relation for the representation of signals in two different general (possibly redundant or incomplete) signal sets. This uncertainty relation is relevant for the analysis of signals containing two distinct features each of which can be described sparsely in a suitable general signal set. Furthermore, the new uncertainty relation is shown to lead to im- proved sparsity thresholds for recovery of signals that are sparse in general dictionaries. Specifically, our results improve on the well-known (1 + 1/d)/2-threshold for dictionaries with coherence d by up to a factor of two. Furthermore, we provide probabilistic recovery guarantees for pairs of general dictionaries that also allow us to understand which parts of a general dictionary one needs to randomize over to "weed out" the sparsity patterns that prohibit breaking the square-root bottleneck.

Connected through hyperedge HE-1 (induced by source paper "sparse representation in fourier and local bases using prosparse a probabilistic analysis"):
  with members:
    - "on sparse representation in fourier and local bases"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "recovery of sparsely corrupted signals"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "almost lossless analog signal separation and probabilistic uncertainty relations"):
  with members:
    - "almost lossless analog compression without phase information"
    - "information theoretic limits of matrix completion"
    - "almost lossless analog signal separation"
    - "sharp recovery bounds for convex demixing with applications"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovery of sparsely corrupted signals"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "recovery of signals with low density"):
  with members:
    - "relaxed recovery conditions for omp ols by exploiting both coherence and decay"
    - "on the theorem of uniform recovery of random sampling matrices"
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "recovery of sparsely corrupted signals"

Connected through hyperedge HE-4 (induced by source paper "probabilistic recovery guarantees for sparsely corrupted signals"):
  with members:
    - "sharp recovery bounds for convex demixing with applications"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"
    - "recovery of sparsely corrupted signals"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "a probabilistic and ripless theory of compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "a short note on compressed sensing with partially known signal support"

Connected through hyperedge HE-5 (induced by source paper "sparse signal recovery in hilbert spaces"):
  with members:
    - "recovery of sparsely corrupted signals"
    - "sparse recovery from combined fusion frame measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "uncertainty relations for shift invariant analog signals"

Connected through hyperedge HE-6 (induced by source paper "stable restoration and separation of approximately sparse signals"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "sparse signal separation in redundant dictionaries"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "exact recoverability from dense corrupted observations via l_1 minimization"
    - "recovery of sparsely corrupted signals"
    - "model based compressive sensing"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "recovery of sparsely corrupted signals"):
  with members:
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "stable restoration and separation of approximately sparse signals"
    - "where is randomness needed to break the square root bottleneck"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)