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: "joint graph decomposition and node labeling by local search"
  Abstract: We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we propose generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define a local search algorithm that converges monotonously to a local optimum, offering a feasible solution at any time. To demonstrate the effectiveness of this algorithm in solving computer vision tasks, we report running times and competitive solutions for two above-mentioned applications.

Connected through hyperedge HE-1 (induced by source paper "pixelwise instance segmentation with a dynamically instantiated network"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "boundary aware instance segmentation"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "end to end instance segmentation and counting with recurrent attention"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"

Connected through hyperedge HE-2 (induced by source paper "a greedy part assignment algorithm for real time multi person 2d pose estimation"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "ssd single shot multibox detector"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "multi person pose estimation with local joint to person associations"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "associative embedding end to end learning for joint detection and grouping"

Connected through hyperedge HE-3 (induced by source paper "instancecut from edges to instances with multicut"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "instance sensitive fully convolutional networks"
    - "pixel level encoding and depth layering for instance level semantic labeling"
    - "bridging category level and instance level semantic image segmentation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"
    - "a multipath network for object detection"

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)