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: "dominant sets for constrained image segmentation"
  Abstract: Image segmentation has come a long way since the early days of computer vision, and still remains a challenging task. Modern variations of the classical (purely bottom-up) approach, involve, e.g., some form of user assistance (interactive segmentation) or ask for the simultaneous segmentation of two or more images (co-segmentation). At an abstract level, all these variants can be thought of as "constrained" versions of the original formulation, whereby the segmentation process is guided by some external source of information. In this paper, we propose a new approach to tackle this kind of problems in a unified way. Our work is 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 that are constrained to contain predefined elements. In particular, we shall focus on interactive segmentation and co-segmentation (in both the unsupervised and the interactive versions). The proposed algorithm can deal naturally with several type of constraints and 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 and constraints.

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"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "interactive image segmentation using constrained dominant sets"

Connected through hyperedge HE-4 (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"
    - "deep image retrieval learning global representations for image search"
    - "interactive image segmentation using constrained dominant sets"
    - "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-5 (induced by source paper "speaker clustering using dominant sets"):
  with members:
    - "voxceleb a large scale speaker identification dataset"
    - "context aware nonnegative matrix factorization clustering"
    - "return of the devil in the details delving deep into convolutional nets"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-6 (induced by source paper "a graph transduction game for multi target tracking"):
  with members:
    - "applications of a graph theoretic based clustering framework in computer vision and pattern recognition"
    - "large scale image geo localization using dominant sets"

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)