human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  Abstract: In this paper, we focus on the semi-supervised person re-identification (Re-ID) case, which only has the intra-camera (within-camera) labels but not inter-camera (cross-camera) labels. In real-world applications, these intra-camera labels can be readily captured by tracking algorithms or few manual annotations, when compared with cross-camera labels. In this case, it is very difficult to explore the relationships between cross-camera persons in the training stage due to the lack of cross-camera label information. To deal with this issue, we propose a novel Progressive Cross-camera Soft-label Learning (PCSL) framework for the semi-supervised person Re-ID task, which can generate cross-camera soft-labels and utilize them to optimize the network. Concretely, we calculate an affinity matrix based on person-level features and adapt them to produce the similarities between cross-camera persons (i.e., cross-camera soft-labels). To exploit these soft-labels to train the network, we investigate the weighted cross-entropy loss and the weighted triplet loss from the classification and discrimination perspectives, respectively. Particularly, the proposed framework alternately generates progressive cross-camera soft-labels and gradually improves feature representations in the whole learning course. Extensive experiments on five large-scale benchmark datasets show that PCSL significantly outperforms the state-of-the-art unsupervised methods that employ labeled source domains or the images generated by the GAN-based models. Furthermore, the proposed method even has a competitive performance with respect to deep supervised Re-ID methods.

Includes members:
  Member "camera adversarial transfer for unsupervised person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial camera alignment network for unsupervised cross camera person re identification")
  Member "imitating targets from all sides an unsupervised transfer learning method for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial camera alignment network for unsupervised cross camera person re identification")
  Member "unsupervised tracklet person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intra camera supervised person re identification")
  Member "a novel unsupervised camera aware domain adaptation framework for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "unsupervised person re identification by soft multilabel learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "invariance matters exemplar memory for domain adaptive person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised discriminative feature learning with state information for person identification")
  Member "label propagation for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification")
  Member "attribute aware attention model for fine grained representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ghostnet more features from cheap operations")

Question: Please tell me which class the source paper 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)