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: "on state estimation with bad data detection"
  Abstract: In this paper, we consider the problem of state estimation through observations possibly corrupted with both bad data and additive observation noises. A mixed $\ell_1$ and $\ell_2$ convex programming is used to separate both sparse bad data and additive noises from the observations. Through using the almost Euclidean property for a linear subspace, we derive a new performance bound for the state estimation error under sparse bad data and additive observation noises. Our main contribution is to provide sharp bounds on the almost Euclidean property of a linear subspace, using the "escape-through-a-mesh" theorem from geometric functional analysis. We also propose and numerically evaluate an iterative convex programming approach to performing bad data detections in nonlinear electrical power networks problems.

Connected through hyperedge HE-1 (induced by source paper "detection of false data injection attacks in smart grid under colored gaussian noise"):
  with members:
    - "vulnerabilities of smart grid state estimation against false data injection attack"

Connected through hyperedge HE-2 (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"
    - "circulant and toeplitz matrices in compressed sensing"
    - "various thresholds for ell_1 optimization in compressed sensing"
    - "highly robust error correction by convex programming"

Connected through hyperedge HE-3 (induced by source paper "outliers and random noises in system identification a compressed sensing approach"):
  with members:
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"):
  with members:
    - "verifiable and computable performance analysis of sparsity recovery"
    - "compressed sensing how sharp is the restricted isometry property"
    - "various thresholds for ell_1 optimization in compressed sensing"

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)