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: "deep constrained dominant sets for person re identification"
  Abstract: In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a {\em constrained clustering optimization} problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale Resnet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.

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"
    - "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"
    - "deep group shuffling random walk for person re identification"

Connected through hyperedge HE-2 (induced by source paper "the group loss for deep metric learning"):
  with members:
    - "ranked list loss for deep metric learning"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "learning with average precision training image retrieval with a listwise loss"
    - "relational knowledge distillation"
    - "divide and conquer the embedding space for metric learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "classification is a strong baseline for deep metric learning"
    - "deep metric learning with bier boosting independent embeddings robustly"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised semantic segmentation using constrained dominant sets"):
  with members:
    - "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"
    - "interactive image segmentation using constrained dominant sets"

Connected through hyperedge HE-4 (induced by source paper "ranking and classification driven feature learning for person re_identification"):
  with members:
    - "ranked list loss for deep metric learning"
    - "omni scale feature learning for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "scpnet spatial channel parallelism network for joint holistic and partial person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"

Connected through hyperedge HE-5 (induced by source paper "multi feature fusion for image retrieval using constrained dominant sets"):
  with members:
    - "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"
    - "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"

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)