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 association learning for unsupervised video person re identification"
  Abstract: Deep learning methods have started to dominate the research progress of video-based person re-identification (re-id). However, existing methods mostly consider supervised learning, which requires exhaustive manual efforts for labelling cross-view pairwise data. Therefore, they severely lack scalability and practicality in real-world video surveillance applications. In this work, to address the video person re-id task, we formulate a novel Deep Association Learning (DAL) scheme, the first end-to-end deep learning method using none of the identity labels in model initialisation and training. DAL learns a deep re-id matching model by jointly optimising two margin-based association losses in an end-to-end manner, which effectively constrains the association of each frame to the best-matched intra-camera representation and cross-camera representation. Existing standard CNNs can be readily employed within our DAL scheme. Experiment results demonstrate that our proposed DAL significantly outperforms current state-of-the-art unsupervised video person re-id methods on three benchmarks: PRID 2011, iLIDS-VID and MARS.

Connected through hyperedge HE-1 (induced by source paper "intra camera supervised person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "unsupervised tracklet person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "weakly supervised person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation in person re id via k reciprocal clustering and large scale heterogeneous environment synthesis"):
  with members:
    - "self similarity grouping a simple unsupervised cross domain adaptation approach for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "support neighbor loss for person re identification"
    - "unsupervised domain adaptive re identification theory and practice"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"

Connected through hyperedge HE-4 (induced by source paper "learning to adapt invariance in memory for person re identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "linkage based face clustering via graph convolution network"
    - "unsupervised tracklet person re identification"
    - "long term feature banks for detailed video understanding"
    - "dissecting person re identification from the viewpoint of viewpoint"
    - "harmonious attention network for person re identification"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-5 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "attribute aware attention model for fine grained representation learning"

Connected through hyperedge HE-6 (induced by source paper "adversarial camera alignment network for unsupervised cross camera person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "multi task mid level feature alignment network for unsupervised cross dataset person re identification"
    - "re identification with consistent attentive siamese networks"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"

Connected through hyperedge HE-7 (induced by source paper "unsupervised tracklet person re identification"):
  with members:
    - "multi level factorisation net for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "domain adaptation through synthesis for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "dynamic label graph matching for unsupervised video re identification"

Connected through hyperedge HE-8 (induced by source paper "unsupervised person re identification by soft multilabel learning"):
  with members:
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "began boundary equilibrium generative adversarial networks"
    - "normface l 2 hypersphere embedding for face verification"
    - "domain adaptation for visual applications a comprehensive survey"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "camera style adaptation for person re identification"

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)