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: "multi shot pedestrian re identification via sequential decision making"
  Abstract: Multi-shot pedestrian re-identification problem is at the core of surveillance video analysis. It matches two tracks of pedestrians from different cameras. In contrary to existing works that aggregate single frames features by time series model such as recurrent neural network, in this paper, we propose an interpretable reinforcement learning based approach to this problem. Particularly, we train an agent to verify a pair of images at each time. The agent could choose to output the result (same or different) or request another pair of images to verify (unsure). By this way, our model implicitly learns the difficulty of image pairs, and postpone the decision when the model does not accumulate enough evidence. Moreover, by adjusting the reward for unsure action, we can easily trade off between speed and accuracy. In three open benchmarks, our method are competitive with the state-of-the-art methods while only using 3% to 6% images. These promising results demonstrate that our method is favorable in both efficiency and performance.

Connected through hyperedge HE-1 (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-2 (induced by source paper "dependency aware attention control for unconstrained face recognition with image sets"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep reinforcement learning a brief survey"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "quality aware network for set to set recognition"
    - "deep reinforcement learning an overview"
    - "deep reinforcement learning attention selection for person re identification"
    - "classification with costly features using deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "attention control with metric learning alignment for image set based recognition"):
  with members:
    - "dependency aware attention control for unconstrained face recognition with image sets"
    - "conservative wasserstein training for pose estimation"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "multicolumn networks for face recognition"
    - "comparator networks"
    - "revisiting temporal modeling for video based person reid"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"

Connected through hyperedge HE-4 (induced by source paper "ordered or orderless a revisit for video based person re identification"):
  with members:
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "spatial temporal synergic residual learning for video person re identification"
    - "deep group shuffling random walk for person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "recurrent neural networks for person re identification revisited"
    - "video based person re identification with accumulative motion context"
    - "person re identification via recurrent feature aggregation"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-5 (induced by source paper "learning to align multi camera domains using part aware clustering for unsupervised video person re identification"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "harmonious attention network for person re identification"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "multi adversarial domain adaptation"
    - "unsupervised person re identification by deep learning tracklet association"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "vrstc occlusion free video person re identification"):
  with members:
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"
    - "re ranking person re identification with k reciprocal encoding"
    - "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"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-7 (induced by source paper "temporal knowledge propagation for image to video person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "vrstc occlusion free video person re identification"
    - "hard aware point to set deep metric for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"
    - "deep mutual learning"
    - "alignedreid surpassing human level performance in person re identification"

Connected through hyperedge HE-8 (induced by source paper "person re identification in videos by analyzing spatio temporal tubes"):
  with members:
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "harmonious attention network for person re identification"
    - "features for multi target multi camera tracking and re identification"
    - "attention aware compositional network for person re identification"
    - "unsupervised cross dataset person re identification by transfer learning of spatial temporal patterns"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing 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)