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: "fast planar correlation clustering for image segmentation"
  Abstract: We describe a new optimization scheme for finding high-quality correlation clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our algorithm on the problem of image segmentation where this approach outperforms existing global optimization techniques in minimizing the objective and is competitive with the state of the art in producing high-quality segmentations.

Connected through hyperedge HE-1 (induced by source paper "a generalized framework for agglomerative clustering of signed graphs applied to instance segmentation"):
  with members:
    - "the mutex watershed and its objective efficient parameter free image partitioning"
    - "instance segmentation by deep coloring"
    - "affinity derivation and graph merge for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "cell detection with star convex polygons"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "recurrent pixel embedding for instance grouping"

Connected through hyperedge HE-2 (induced by source paper "the mutex watershed and its objective efficient parameter free image partitioning"):
  with members:
    - "learned watershed end to end learning of seeded segmentation"
    - "superhuman accuracy on the snemi3d connectomics challenge"
    - "learning steerable filters for rotation equivariant cnns"
    - "anisotropic em segmentation by 3d affinity learning and agglomeration"
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "fusionnet a deep fully residual convolutional neural network for image segmentation in connectomics"
    - "a multi cut formulation for joint segmentation and tracking of multiple objects"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-3 (induced by source paper "massively parallel benders decomposition for correlation clustering"):
  with members:
    - "accelerating message passing for map with benders decomposition"
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "next generation multicuts for semi planar graphs"
    - "parallel correlation clustering on big graphs"

Connected through hyperedge HE-4 (induced by source paper "phase contrast microscopy cell populationsegmentation a survey"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "transductive rademacher complexity and its applications"

Connected through hyperedge HE-5 (induced by source paper "end to end learning for graph decomposition"):
  with members:
    - "end to end learning of multi sensor 3d tracking by detection"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "optnet differentiable optimization as a layer in neural networks"
    - "towards accurate multi person pose estimation in the wild"
    - "deep network flow for multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "efficient algorithms for moral lineage tracing"):
  with members:
    - "the minimum cost connected subgraph problem in medical image analysis"
    - "arttrack articulated multi person tracking in the wild"
    - "moral lineage tracing"
    - "lifting of multicuts"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "large scale correlation clustering optimization"

Connected through hyperedge HE-7 (induced by source paper "efficient column generation for cell detection and segmentation"):
  with members:
    - "multi person pose estimation via column generation"
    - "u net convolutional networks for biomedical image segmentation"
    - "next generation multicuts for semi planar graphs"
    - "monocular object instance segmentation and depth ordering with cnns"
    - "tracking objects with higher order interactions using delayed column generation"

Connected through hyperedge HE-8 (induced by source paper "better image segmentation by exploiting dense semantic predictions"):
  with members:
    - "semantic image segmentation via deep parsing network"
    - "holistically nested edge detection"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"
    - "pushing the boundaries of boundary detection using deep learning"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "microsoft coco common objects in context"
    - "fast edge detection using structured forests"
    - "fully convolutional networks for semantic segmentation"

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)