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: "projection based and look ahead strategies for atom selection"
  Abstract: In this paper, we improve iterative greedy search algorithms in which atoms are selected serially over iterations, i.e., one-by-one over iterations. For serial atom selection, we devise two new schemes to select an atom from a set of potential atoms in each iteration. The two new schemes lead to two new algorithms. For both the algorithms, in each iteration, the set of potential atoms is found using a standard matched filter. In case of the first scheme, we propose an orthogonal projection strategy that selects an atom from the set of potential atoms. Then, for the second scheme, we propose a look-ahead strategy such that the selection of an atom in the current iteration has an effect on the future iterations. The use of look-ahead strategy requires a higher computational resource. To achieve a tradeoff between performance and complexity, we use the two new schemes in cascade and develop a third new algorithm. Through experimental evaluations, we compare the pro posed algorithms with existing greedy search and convex relaxation algorithms.

Connected through hyperedge HE-1 (induced by source paper "design and analysis of a greedy pursuit for distributed compressed sensing"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "greedy sparsity promoting algorithms for distributed learning"
    - "analysis of democratic voting principles used in distributed greedy algorithms"
    - "distributed compressed sensing for static and time varying networks"
    - "distributed greedy pursuit algorithms"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "typical l 1 recovery limit of sparse vectors represented by concatenations of random orthogonal matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "analysis by synthesis based quantization of compressed sensing measurements"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "universal rate efficient scalar quantization"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "power constrained sparse gaussian linear dimensionality reduction over noisy channels"):
  with members:
    - "joint source channel vector quantization for compressed sensing"
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "estimating unknown sparsity in compressed sensing"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "on mmse and map denoising under sparse representation modeling over a unitary dictionary"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-4 (induced by source paper "channel optimized vector quantizer design for compressed sensing measurements"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "analysis by synthesis based quantization of compressed sensing measurements"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "universal rate efficient scalar quantization"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-5 (induced by source paper "homotopy based algorithms for ell_0 regularized least squares"):
  with members:
    - "sparse signal estimation by maximally sparse convex optimization"
    - "universal regularizers for robust sparse coding and modeling"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-6 (induced by source paper "analysis by synthesis quantization for compressed sensing measurements"):
  with members:
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "distributed scalar quantization for computing high resolution analysis and extensions"
    - "universal rate efficient scalar quantization"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-7 (induced by source paper "distributed greedy pursuit algorithms"):
  with members:
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "distributed sparse signal recovery for sensor networks"
    - "distributed compressive 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.SY(Systems and Control)