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: "probabilistic recovery guarantees for sparsely corrupted signals"
  Abstract: We consider the recovery of sparse signals subject to sparse interference, as introduced in Studer et al., IEEE Trans. IT, 2012. We present novel probabilistic recovery guarantees for this framework, covering varying degrees of knowledge of the signal and interference support, which are relevant for a large number of practical applications. Our results assume that the sparsifying dictionaries are characterized by coherence parameters and we require randomness only in the signal and/or interference. The obtained recovery guarantees show that one can recover sparsely corrupted signals with overwhelming probability, even if the sparsity of both the signal and interference scale (near) linearly with the number of measurements.

Connected through hyperedge HE-1 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "modulated unit norm tight frames for compressed sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "convolutional compressed sensing using deterministic sequences"
    - "the restricted isometry property for random block diagonal matrices"
    - "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"

Connected through hyperedge HE-2 (induced by source paper "uncertainty relations and sparse signal recovery"):
  with members:
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "stable recovery of structured signals from corrupted sub gaussian measurements"):
  with members:
    - "corrupted sensing with sub gaussian measurements"
    - "on the phase transition of corrupted sensing"
    - "data recovery from corrupted observations via l1 minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "on the phase transition of corrupted sensing"):
  with members:
    - "corrupted sensing with sub gaussian measurements"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "corrupted sensing with sub gaussian measurements"):
  with members:
    - "on the phase transition of corrupted sensing"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "compressed sensing with corrupted observations"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "recovery of sparsely corrupted signals"
    - "a probabilistic and ripless theory of compressed sensing"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (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"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "recovery of sparsely corrupted signals"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (induced by source paper "data recovery from corrupted observations via l1 minimization"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "a probabilistic and ripless theory of compressed sensing"
    - "robust principal component analysis"

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)