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: "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
  Abstract: Compressed sensing is a developing field aiming at the reconstruction of sparse signals acquired in reduced dimensions, which make the recovery process under-determined. The required solution is the one with minimum @?"0 norm due to sparsity, however it is not practical to solve the @?"0 minimization problem. Commonly used techniques include @?"1 minimization, such as Basis Pursuit (BP) and greedy pursuit algorithms such as Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP). This manuscript proposes a novel semi-greedy recovery approach, namely A* Orthogonal Matching Pursuit (A*OMP). A*OMP performs A* search to look for the sparsest solution on a tree whose paths grow similar to the Orthogonal Matching Pursuit (OMP) algorithm. Paths on the tree are evaluated according to a cost function, which should compensate for different path lengths. For this purpose, three different auxiliary structures are defined, including novel dynamic ones. A*OMP also incorporates pruning techniques which enable practical applications of the algorithm. Moreover, the adjustable search parameters provide means for a complexity-accuracy trade-off. We demonstrate the reconstruction ability of the proposed scheme on both synthetically generated data and images using Gaussian and Bernoulli observation matrices, where A*OMP yields less reconstruction error and higher exact recovery frequency than BP, OMP and SP. Results also indicate that novel dynamic cost functions provide improved results as compared to a conventional choice.

Connected through hyperedge HE-1 (induced by source paper "best first search algorithm for non convex sparse minimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "anytime heuristic search"

Connected through hyperedge HE-2 (induced by source paper "improving a omp"):
  with members:
    - "distributed greedy pursuit algorithms"
    - "compressed sensing signal recovery via forward backward pursuit"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "sparse signal reconstruction via iterative support detection"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "multiuser media based modulation for massive mimo systems"):
  with members:
    - "on media based modulation using rf mirrors"
    - "generalized spatial modulation in large scale multiuser mimo systems"
    - "media based mimo a new frontier in wireless communications"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "comments on multipath matching pursuit by kwon wang and shim"):
  with members:
    - "improving a omp"

Connected through hyperedge HE-5 (induced by source paper "multi branch matching pursuit with applications to mimo radar"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "analysis of sparse mimo radar"
    - "remote sensing via ell_1 minimization"
    - "mimo radar using compressive sampling"
    - "rank awareness in joint sparse recovery"
    - "message passing algorithms for compressed sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "online recovery guarantees and analytical results for omp"):
  with members:
    - "compressed sensing signal recovery via forward backward pursuit"
    - "generalized orthogonal matching pursuit"
    - "structured compressed sensing from theory to applications"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "model based compressive sensing"
    - "optimally tuned iterative reconstruction algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for 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)