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: "interactive image segmentation using constrained dominant sets"
  Abstract: We propose a new approach to interactive image segmentation based on some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. In particular, we show that by properly controlling a regularization parameter which determines the structure and the scale of the underlying problem, we are in a position to extract groups of dominant-set clusters which are constrained to contain user-selected elements. The resulting algorithm can deal naturally with any type of input modality, including scribbles, sloppy contours, and bounding boxes, and is able to robustly handle noisy annotations on the part of the user. Experiments on standard benchmark datasets show the effectiveness of our approach as compared to state-of-the-art algorithms on a variety of natural images under several input conditions.

Connected through hyperedge HE-1 (induced by source paper "constrained dominant sets and its applications in computer vision"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "deep constrained dominant sets for person re identification"
    - "weakly supervised semantic segmentation using constrained dominant sets"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning neural models for end to end clustering"
    - "transductive label augmentation for improved deep network learning"
    - "multi feature fusion for image retrieval using constrained dominant sets"

Connected through hyperedge HE-2 (induced by source paper "deep constrained dominant sets for person re identification"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "harmonious attention network for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised semantic segmentation using constrained dominant sets"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi feature fusion for image retrieval using constrained dominant sets"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "dominant sets for constrained image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"

Connected through hyperedge HE-4 (induced by source paper "moe spnet a mixture of experts scene parsing network"):
  with members:
    - "loss max pooling for semantic image segmentation"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "semantic understanding of scenes through the ade20k dataset"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "semantic image segmentation via deep parsing network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "multi feature fusion for image retrieval using constrained dominant sets"):
  with members:
    - "deep constrained dominant sets for person re identification"
    - "deep group shuffling random walk for person re identification"
    - "part based weighting aggregation of deep convolutional features for image retrieval"
    - "dominant sets for constrained image segmentation"
    - "deep image retrieval learning global representations for image search"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "cross dimensional weighting for aggregated deep convolutional features"

Connected through hyperedge HE-6 (induced by source paper "multi target tracking in multiple non overlapping cameras using constrained dominant sets"):
  with members:
    - "large scale image geo localization using dominant sets"
    - "performance measures and a data set for multi target multi camera tracking"
    - "deep residual learning for image recognition"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "alid scalable dominant cluster detection"

Connected through hyperedge HE-7 (induced by source paper "adaptive regularization of some inverse problems in image analysis"):
  with members:
    - "learning raw image denoising with bayer pattern unification and bayer preserving augmentation"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-8 (induced by source paper "dominant sets for constrained image segmentation"):
  with members:
    - "multi target tracking in multiple non overlapping cameras using constrained dominant sets"
    - "loosecut interactive image segmentation with loosely bounded boxes"

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)