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: "an automatic solver for very large jigsaw puzzles using genetic algorithms"
  Abstract: In this paper we propose the first effective genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel crossover procedure that merges two "parent" solutions to an improved "child" configuration by detecting, extracting, and combining correctly assembled puzzle segments. The solver proposed exhibits state-of-the-art performance, as far as handling previously attempted puzzles more accurately and efficiently, as well puzzle sizes that have not been attempted before. The extended experimental results provided in this paper include, among others, a thorough inspection of up to 30,745-piece puzzles (compared to previous attempts on 22,755-piece puzzles), using a considerably faster concurrent implementation of the algorithm. Furthermore, we explore the impact of different phases of the novel crossover operator by experimenting with several variants of the GA. Finally, we compare different fitness functions and their effect on the overall results of the GA-based solver.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "grayscale based block scrambling image encryption for social networking services"):
  with members:
    - "a generalized genetic algorithm based solver for very large jigsaw puzzles of complex types"
    - "cryptanalyzing an image scrambling encryption algorithm of pixel bits"
    - "a classification engine for image ballistics of social data"

Connected through hyperedge HE-3 (induced by source paper "security evaluation for block scrambling based image encryption including jpeg distortion against jigsaw puzzle solver attacks"):
  with members:
    - "cryptanalyzing an image scrambling encryption algorithm of pixel bits"
    - "solving jigsaw puzzles with linear programming"

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)