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 generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
  Abstract: In this paper we introduce new types of square-piece jigsaw puzzles, where in addition to the unknown location and orientation of each piece, a piece might also need to be flipped. These puzzles, which are associated with a number of real world problems, are considerably harder, from a computational standpoint. Specifically, we present a novel generalized genetic algorithm (GA)-based solver that can handle puzzle pieces of unknown location and orientation (Type 2 puzzles) and (two-sided) puzzle pieces of unknown location, orientation, and face (Type 4 puzzles). To the best of our knowledge, our solver provides a new state-of-the-art, solving previously attempted puzzles faster and far more accurately, handling puzzle sizes that have never been attempted before, and assembling the newly introduced two-sided puzzles automatically and effectively. This paper also presents, among other results, the most extensive set of experimental results, compiled as of yet, on Type 2 puzzles.

Connected through hyperedge HE-1 (induced by source paper "domain generalization by solving jigsaw puzzles"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "partial adversarial domain adaptation"
    - "image reassembly combining deep learning and shortest path problem"
    - "adaptive deep learning through visual domain localization"
    - "generalizing across domains via cross gradient training"
    - "domain generalization with domain specific aggregation modules"

Connected through hyperedge HE-2 (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"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "dnn buddies a deep neural network based estimation metric for the jigsaw puzzle problem"
    - "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-3 (induced by source paper "solving jigsaw puzzles by the graph connection laplacian"):
  with members:
    - "an automatic solver for very large jigsaw puzzles using genetic algorithms"
    - "dnn buddies a deep neural network based estimation metric for the jigsaw puzzle problem"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "grayscale based block scrambling image encryption for social networking services"):
  with members:
    - "cryptanalyzing an image scrambling encryption algorithm of pixel bits"
    - "an automatic solver for very large jigsaw puzzles using genetic algorithms"
    - "a classification engine for image ballistics of social data"

Connected through hyperedge HE-5 (induced by source paper "an automatic solver for very large jigsaw puzzles using genetic algorithms"):
  with members:
    - "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"

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)