human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "massively parallel benders decomposition for correlation clustering"):
  Abstract: We tackle the problem of graph partitioning for image segmentation using correlation clustering (CC), which we treat as an integer linear program (ILP). We reformulate optimization in the ILP so as to admit efficient optimization via Benders decomposition, a classic technique from operations research. Our Benders decomposition formulation has many subproblems, each associated with a node in the CC instance's graph, which are solved in parallel. Each Benders subproblem enforces the cycle inequalities corresponding to the negative weight edges attached to its corresponding node in the CC instance. We generate Magnanti-Wong Benders rows in addition to standard Benders rows, to accelerate optimization. Our Benders decomposition approach provides a promising new avenue to accelerate optimization for CC, and allows for massive parallelization.

Includes members:
  Member "accelerating message passing for map with benders decomposition":
    also connected through other hyperedges:
      (none)
  Member "exploiting skeletal structure in computer vision annotation with benders decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "accelerating message passing for map with benders decomposition")
  Member "a message passing algorithm for the minimum cost multicut problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end learning for graph decomposition")
  Member "deepercut a deeper stronger and faster multi person pose estimation model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "deepcut joint subset partition and labeling for multi person pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation")
  Member "efficient decomposition of image and mesh graphs by lifted multicuts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep affinity net instance segmentation via affinity")
  Member "next generation multicuts for semi planar graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient column generation for cell detection and segmentation")
  Member "parallel correlation clustering on big graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fair correlation clustering")

Question: Please tell me which class the source paper 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)