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: "region based quality estimation network for large scale person re identification"
  Abstract: One of the major restrictions on the performance of video-based person re-id is partial noise caused by occlusion, blur and illumination. Since different spatial regions of a single frame have various quality, and the quality of the same region also varies across frames in a tracklet, a good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate the influence of an image region with poor quality. To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Compared with other feature extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83% on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate the lack of clean large-scale person re-id datasets for the community, this paper also contributes a new high-quality dataset, named "Labeled Pedestrian in the Wild (LPW)" which contains 7,694 tracklets with over 590,000 images. Despite its relatively large scale, the annotations also possess high cleanliness. Moreover, it's more challenging in the following aspects: the age of characters varies from childhood to elderhood; the postures of people are diverse, including running and cycling in addition to the normal walking state.

Connected through hyperedge HE-1 (induced by source paper "scan self and collaborative attention network for video person re identification"):
  with members:
    - "person re identification in videos by analyzing spatio temporal tubes"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "person re identification by camera correlation aware feature augmentation"
    - "learning view specific deep networks for person re identification"
    - "harmonious attention network for person re identification"
    - "efficient and deep person re identification using multi level similarity"

Connected through hyperedge HE-2 (induced by source paper "adaptive graph representation learning for video person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interaction and aggregation network for person re identification"
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "learning context graph for person search"
    - "omni scale feature learning for person re identification"
    - "video person re identification by temporal residual learning"
    - "scan self and collaborative attention network for video person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-3 (induced by source paper "occlusion robust online multi object visual tracking using a gm phd filter with a cnn based re identification"):
  with members:
    - "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "online multiple pedestrian tracking using deep temporal appearance matching association"
    - "machine learning methods for solving assignment problems in multi target tracking"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person transfer gan to bridge domain gap for person re identification"

Connected through hyperedge HE-4 (induced by source paper "robust online multi target visual tracking using a hisp filter with discriminative deep appearance learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "development of a n type gm phd filter for multiple target multiple type visual tracking"
    - "long term correlation tracking using multi layer hybrid features in sparse and dense environments"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "focal loss for dense object detection"
    - "person transfer gan to bridge domain gap for person re identification"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-5 (induced by source paper "person re identification with deep similarity guided graph neural network"):
  with members:
    - "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"
    - "multi channel pyramid person matching network for person re identification"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "deep group shuffling random walk for person re identification"
    - "vip cnn visual phrase guided convolutional neural network"
    - "random erasing data augmentation"

Connected through hyperedge HE-6 (induced by source paper "deep metric learning by online soft mining and class aware attention"):
  with members:
    - "learn to pay attention"
    - "video based person re identification with accumulative motion context"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "smart mining for deep metric learning"
    - "understanding deep learning requires rethinking generalization"
    - "learning deep embeddings with histogram loss"
    - "learning a metric embedding for face recognition using the multibatch method"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-7 (induced by source paper "sta spatial temporal attention for large scale video based person re identification"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "a structured self attentive sentence embedding"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "performance measures and a data set for multi target multi camera tracking"

Connected through hyperedge HE-8 (induced by source paper "multi shot person re identification through set distance with visual distributional representation"):
  with members:
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "part aligned bilinear representations for person re identification"
    - "revisiting temporal modeling for video based person reid"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "densely connected convolutional networks"

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)