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: "recovering jointly sparse signals via joint basis pursuit"
  Abstract: This work considers recovery of signals that are sparse over two bases. For instance, a signal might be sparse in both time and frequency, or a matrix can#R##N#be low rank and sparse simultaneously. To facilitate recovery, we consider minimizing the sum of the l_1-norms that correspond to each basis, which is a tractable convex approach. We find novel optimality conditions which indicates a gain over traditional approaches where l_1 minimization is done over only one basis. Next, we analyze these optimality conditions for the particular case of time-frequency bases. Denoting sparsity in the first and second bases by k_1,k_2 respectively, we show that, for a general class of signals, using this approach, one requires as small as O(max{k_1,k_2} log log n) measurements for successful recovery hence overcoming the classical requirement of Θ(min{k_1,k_2} log (n/(min{k_1,k_2})) for l#R##N#_1 minimization when k_1 ≈ k_2. Extensive simulations show that, our analysis is approximately tight.

Connected through hyperedge HE-1 (induced by source paper "simultaneously structured models with application to sparse and low rank matrices"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "tensor based formulation and nuclear norm regularization for multienergy computed tomography"
    - "living on the edge phase transitions in convex programs with random data"
    - "gespar efficient phase retrieval of sparse signals"
    - "the squared error of generalized lasso a precise analysis"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "phase retrieval stability and recovery guarantees"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"

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)