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: "highly robust error correction by convex programming"
  Abstract: This paper discusses a stylized communications problem where one wishes to transmit a real-valued signal x in R^n (a block of n pieces of information) to a remote receiver. We ask whether it is possible to transmit this information reliably when a fraction of the transmitted codeword is corrupted by arbitrary gross errors, and when in addition, all the entries of the codeword are contaminated by smaller errors (e.g. quantization errors). #R##N#We show that if one encodes the information as Ax where A is a suitable m by n coding matrix (m >= n), there are two decoding schemes that allow the recovery of the block of n pieces of information x with nearly the same accuracy as if no gross errors occur upon transmission (or equivalently as if one has an oracle supplying perfect information about the sites and amplitudes of the gross errors). Moreover, both decoding strategies are very concrete and only involve solving simple convex optimization programs, either a linear program or a second-order cone program. We complement our study with numerical simulations showing that the encoder/decoder pair performs remarkably well.

Connected through hyperedge HE-1 (induced by source paper "guarantees of total variation minimization for signal recovery"):
  with members:
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "living on the edge phase transitions in convex programs with random data"
    - "stable recovery with analysis decomposable priors"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "analysis ell_1 recovery with frames and gaussian measurements"
    - "stable image reconstruction using total variation minimization"
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"

Connected through hyperedge HE-2 (induced by source paper "robust error correction for real valued signals via message passing decoding and spatial coupling"):
  with members:
    - "a simple proof of threshold saturation for coupled scalar recursions"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "subsampling at information theoretically optimal rates"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "compressed sensing with sparse structured matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "toeplitz matrix based sparse error correction in system identification outliers and random noises"):
  with members:
    - "outliers and random noises in system identification a compressed sensing approach"
    - "on state estimation with bad data detection"
    - "circulant and toeplitz matrices in compressed sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "on state estimation with bad data detection"):
  with members:
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "streaming graph computations with a helpful advisor"):
  with members:
    - "computing the diameter polynomially faster than apsp"

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)