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: "a novel unsupervised camera aware domain adaptation framework for person re identification"
  Abstract: Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain. They are addressed in this paper from the perspective of representation learning. For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains. For the second issue, we exploit the temporal continuity in each camera of target domain to create discriminative information. This is implemented by dynamically generating online triplets within each batch, in order to maximally take advantage of the steadily improved feature representation in training process. Together, the above two methods give rise to a novel unsupervised deep domain adaptation framework for person Re-ID. Experiments and ablation studies on benchmark datasets demonstrate its superiority and interesting properties.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "learning to adapt invariance in memory for person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "boosting self supervised learning via knowledge transfer"

Connected through hyperedge HE-2 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"):
  with members:
    - "deep self learning from noisy labels"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "when does label smoothing help"
    - "unsupervised person re identification by soft multilabel learning"
    - "dual student breaking the limits of the teacher in semi supervised learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-3 (induced by source paper "structured domain adaptation for unsupervised person re identification"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification"
    - "self training with progressive augmentation for unsupervised cross domain person re identification"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep adaptive feature embedding with local sample distributions for person re identification"

Connected through hyperedge HE-5 (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"
    - "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"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"

Connected through hyperedge HE-6 (induced by source paper "greyreid a two stream deep framework with rgb grey information for person re identification"):
  with members:
    - "unsupervised person image generation with semantic parsing transformation"
    - "aanet attribute attention network for person re identifications"
    - "attribute aware attention model for fine grained representation learning"
    - "ca3net contextual attentional attribute appearance network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-7 (induced by source paper "visual textual association with hardest and semi hard negative pairs mining for person search"):
  with members:
    - "learning context graph for person search"
    - "saliency guided attention network for image sentence matching"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "what can help pedestrian detection"
    - "vse improving visual semantic embeddings with hard negatives"
    - "neural person search machines"

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)