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: "cooperative greedy pursuit strategies for sparse signal representation by partitioning"
  Abstract: Cooperative Greedy Pursuit Strategies are considered for approximating a signal partition subjected to a global constraint on sparsity. The approach aims at producing a high quality sparse approximation of the whole signal, using highly coherent redundant dictionaries. The cooperation takes place by ranking the partition units for their sequential stepwise approximation, and is realized by means of i)forward steps for the upgrading of an approximation and/or ii) backward steps for the corresponding downgrading. The advantage of the strategy is illustrated by producing high quality approximations of music signals using redundant trigonometric dictionaries. In addition to rendering stunning improvements in sparsity with respect to the concomitant trigonometric basis, these dictionaries enable a fast implementation of the approach via the Fast Fourier Transform.

Connected through hyperedge HE-1 (induced by source paper "analysis of a low memory implementation of the orthogonal matching pursuit greedy strategy"):
  with members:
    - "improving a omp"
    - "a dedicated greedy pursuit algorithm for sparse spectral representation of music sound"
    - "hierarchized block wise image approximation by greedy pursuit strategies"

Connected through hyperedge HE-2 (induced by source paper "effective sparse representation of x ray medical images"):
  with members:
    - "low memory implementation of orthogonal matching pursuit like greedy algorithms analysis and applications"
    - "hierarchized block wise image approximation by greedy pursuit strategies"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "low memory implementation of orthogonal matching pursuit like greedy algorithms analysis and applications"):
  with members:
    - "a dedicated greedy pursuit algorithm for sparse spectral representation of music sound"
    - "hierarchized block wise image approximation by greedy pursuit strategies"

Connected through hyperedge HE-4 (induced by source paper "trigonometric dictionary based codec for music compression with high quality recovery"):
  with members:
    - "hierarchized block wise image approximation by greedy pursuit strategies"

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.DS(Data Structures and Algorithms)