human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "stable restoration and separation of approximately sparse signals"):
  Abstract: This paper develops new theory and algorithms to recover signals that are approximately sparse in some general dictionary (i.e., a basis, frame, or over-/incomplete matrix) but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise. The algorithms and analytical recovery conditions consider varying degrees of signal and interference support-set knowledge. Particular applications covered by the proposed framework include the restoration of signals impaired by impulse noise, narrowband interference, or saturation/clipping, as well as image in-painting, super-resolution, and signal separation. Two application examples for audio and image restoration demonstrate the efficacy of the approach.

Includes members:
  Member "sharp rip bound for sparse signal and low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "uncertainty relations and sparse signal recovery for pairs of general signal sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse representation in fourier and local bases using prosparse a probabilistic analysis")
  Member "probabilistic recovery guarantees for sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing")
  Member "sparse signal separation in redundant dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast signal separation of 2 d sparse mixture via approximate message passing")
  Member "compressed sensing and matrix completion with constant proportion of corruptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks")
  Member "exact recoverability from dense corrupted observations via l_1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high dimensional robust sparse regression")
  Member "recovery of sparsely corrupted signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "metrics for evaluating the efficiency of compressing sensing techniques")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compressed sensing with invertible generative models and dependent noise")

Question: Please tell me which class the source paper 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)