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: "hierarchized block wise image approximation by greedy pursuit strategies"
  Abstract: An approach for effective implementation of greedy selection methodologies, to approximate an image partitioned into blocks, is proposed. The method is specially designed for approximating partitions on a transformed image. It evolves by selecting, at each iteration step, i) the elements for approximating each of the blocks partitioning the image and ii) the hierarchized sequence in which the blocks are approximated to reach the required global condition on sparsity.

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"
    - "cooperative greedy pursuit strategies for sparse signal representation by partitioning"

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"
    - "cooperative greedy pursuit strategies for sparse signal representation by partitioning"
    - "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"
    - "cooperative greedy pursuit strategies for sparse signal representation by partitioning"

Connected through hyperedge HE-4 (induced by source paper "trigonometric dictionary based codec for music compression with high quality recovery"):
  with members:
    - "cooperative greedy pursuit strategies for sparse signal representation by partitioning"

Connected through hyperedge HE-5 (induced by source paper "cooperative greedy pursuit strategies for sparse signal representation by partitioning"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"

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.CV(Computer Vision and Pattern Recognition)