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: "sample complexity of total variation minimization"
  Abstract: This work considers the use of Total variation (TV) minimization in the recovery of a given gradient sparse vector from Gaussian linear measurements. It has been shown in recent studies that there exist a sharp phase transition behavior in TV minimization in asymptotic regimes. The phase transition curve specifies the boundary of success and failure of TV minimization for large number of measurements. It is a challenging task to obtain a theoretical bound that reflects this curve. In this work, we present a novel upper-bound that suitably approximates this curve and is asymptotically sharp. Numerical results show that our bound is closer to the empirical TV phase transition curve than the previously known bound obtained by Kabanava.

Connected through hyperedge HE-1 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization"):
  with members:
    - "on the error in phase transition computations for compressed sensing"
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "on the effective measure of dimension in the analysis cosparse model"

Connected through hyperedge HE-2 (induced by source paper "improved recovery of analysis sparse vectors in presence of prior information"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "on the error in phase transition computations for compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "living on the edge phase transitions in convex programs with random data"

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)