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: "compressive sensing over the grassmann manifold a unified geometric framework"
  Abstract: l_1 minimization is often used for finding the sparse solutions of an under-determined linear system. In this paper we focus on finding sharp performance bounds on recovering approximately sparse signals using l_1 minimization, possibly under noisy measurements. While the restricted isometry property is powerful for the analysis of recovering approximately sparse signals with noisy measurements, the known bounds on the achievable sparsity (The "sparsity" in this paper means the size of the set of nonzero or significant elements in a signal vector.) level can be quite loose. The neighborly polytope analysis which yields sharp bounds for ideally sparse signals cannot be readily generalized to approximately sparse signals. Starting from a necessary and sufficient condition, the "balancedness" property of linear subspaces, for achieving a certain signal recovery accuracy, we give a unified null space Grassmann angle-based geometric framework for analyzing the performance of l_1 minimization. By investigating the "balancedness" property, this unified framework characterizes sharp quantitative tradeoffs between the considered sparsity and the recovery accuracy of the l_1 optimization. As a consequence, this generalizes the neighborly polytope result for ideally sparse signals. Besides the robustness in the "strong" sense for all sparse signals, we also discuss the notions of "weak" and "sectional" robustness. Our results concern fundamental properties of linear subspaces and so may be of independent mathematical interest.

Connected through hyperedge HE-1 (induced by source paper "generalized approximate message passing for cosparse analysis compressive sensing"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "variational free energies for compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "on convergence of approximate message passing"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "sparsity averaging for compressive imaging"

Connected through hyperedge HE-2 (induced by source paper "technical report observability of a linear system under sparsity constraints"):
  with members:
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"):
  with members:
    - "message passing de quantization with applications to compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "generalized approximate message passing for estimation with random linear mixing"

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)