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: "data recovery from corrupted observations via l1 minimization"
  Abstract: This paper studies the problem of recovering a signal vector and the corrupted noise vector from a collection of corrupted linear measurements through the solution of a l1 minimization, where the sensing matrix is a partial Fourier matrix whose rows are selected randomly and uniformly from rows of a full Fourier matrix. After choosing the parameter in the l1 minimization appropriately, we show that the recovery can be successful even when a constant fraction of the measurements are arbitrarily corrupted, moreover, the proportion of corrupted measurement can grows arbitrarily close to 1, provided that the signal vector is sparse enough. The upper-bound on the sparsity of the signal vector required in this paper is asymptotically optimal and is better than those achieved by recent literatures [1, 2] by a ln(n) factor. Furthermore, the assumptions we impose on the signal vector and the corrupted noise vector are loosest comparing to the existing literatures [1-3], which lenders our recovery guarantees are more applicable. Extensive numerical experiments based on synthesis as well as real world data are presented to verify the conclusion of the proposed theorem and to demonstrate the potential of the l1 minimization framework.

Connected through hyperedge HE-1 (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"
    - "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"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "stable restoration and separation of approximately sparse signals"

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)