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: "dnn buddies a deep neural network based estimation metric for the jigsaw puzzle problem"
  Abstract: This paper introduces the first deep neural network-based estimation metric for the jigsaw puzzle problem. Given two puzzle piece edges, the neural network predicts whether or not they should be adjacent in the correct assembly of the puzzle, using nothing but the pixels of each piece. The proposed metric exhibits an extremely high precision even though no manual feature extraction is performed. When incorporated into an existing puzzle solver, the solution’s accuracy increases significantly, achieving thereby a new state-of-the-art standard.

Connected through hyperedge HE-1 (induced by source paper "a novel hybrid scheme using genetic algorithms and deep learning for the reconstruction of portuguese tile panels"):
  with members:
    - "deeppermnet visual permutation learning"
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"
    - "a genetic algorithm based solver for very large jigsaw puzzles"

Connected through hyperedge HE-2 (induced by source paper "solving jigsaw puzzles by the graph connection laplacian"):
  with members:
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "an automatic solver for very large jigsaw puzzles using genetic algorithms"
    - "solving jigsaw puzzles with linear programming"
    - "a global approach for solving edge matching puzzles"
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"

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)