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: "sparse approximation based on a random overcomplete basis"
  Abstract: We discuss a strategy of sparse approximation that is based on the use of an overcomplete basis, and evaluate its performance when a random matrix is used as this basis. A small combination of basis vectors is chosen from a given overcomplete basis, according to a given compression rate, such that they compactly represent the target data with as small a distortion as possible. As a selection method, we study the - and -based methods, which employ the exhaustive search and -norm regularization techniques, respectively. The performance is assessed in terms of the trade-off relation between the distortion and the compression rate. First, we evaluate the performance analytically in the case that the methods are carried out ideally, using methods of statistical mechanics. The analytical result is then confirmed by performing numerical experiments on finite size systems, and extrapolating the results to the infinite-size limit. Our result clarifies the fact that the -based method greatly outperforms the -based one. An interesting outcome of our analysis is that any small value of distortion is achievable for any fixed compression rate r in the large-size limit of the overcomplete basis, for both the - and -based methods. The difference between these two methods is manifested in the size of the overcomplete basis that is required in order to achieve the desired value for the distortion. As the desired distortion decreases, the required size grows in a polynomial and an exponential manners for the - and -based methods, respectively. Second, we examine the practical performances of two well-known algorithms, orthogonal matching pursuit and approximate message passing, when they are used to execute the - and -based methods, respectively. Our examination shows that orthogonal matching pursuit achieves a much better performance than the exact execution of the -based method, as well as approximate message passing. However, regarding the -based method, there is still room to design more effective greedy algorithms than orthogonal matching pursuit. Finally, we evaluate the performances of the algorithms when they are applied to image data compression.

Connected through hyperedge HE-1 (induced by source paper "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"):
  with members:
    - "approximate message passing with restricted boltzmann machine priors"
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "bayesian fusion of multi band images"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "cross validation in lasso and its acceleration"):
  with members:
    - "sparse modeling for image and vision processing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "evaluation of generalized degrees of freedom for sparse estimation by replica method"):
  with members:
    - "cross validation in lasso and its acceleration"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sampling approach to sparse approximation problem determining degrees of freedom by simulated annealing"):
  with members:
    - "sparse approximation problem how rapid simulated annealing succeeds and fails"
    - "cross validation in lasso and its acceleration"

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)