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 genetic algorithm based solver for very large jigsaw puzzles"
  Abstract: In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance solving previously attempted puzzles faster and far more accurately, and also puzzles of size never before attempted. Other contributions include the creation of a benchmark of large images, previously unavailable. We share the data sets and all of our results for future testing and comparative evaluation of jigsaw puzzle solvers.

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"
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deeppermnet visual permutation learning"):
  with members:
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "adversarial feature learning"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "end to end localization and ranking for relative attributes"
    - "self supervised video representation learning with odd one out networks"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-4 (induced by source paper "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"):
  with members:
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"

Connected through hyperedge HE-5 (induced by source paper "an automatic solver for very large jigsaw puzzles using genetic algorithms"):
  with members:
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"

Connected through hyperedge HE-6 (induced by source paper "dnn buddies a deep neural network based estimation metric for the jigsaw puzzle problem"):
  with members:
    - "genetic algorithm based solver for very large multiple jigsaw puzzles of unknown dimensions and piece orientation"

Connected through hyperedge HE-7 (induced by source paper "image reconstruction from bag of visual words"):
  with members:
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-8 (induced by source paper "a global approach for solving edge matching puzzles"):
  with members:
    - "playing games with algorithms algorithmic combinatorial game theory"

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)