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: "revisiting temporal modeling for video based person reid"
  Abstract: Video-based person reID is an important task, which has received much attention in recent years due to the increasing demand in surveillance and camera networks. A typical video-based person reID system consists of three parts: an image-level feature extractor (e.g. CNN), a temporal modeling method to aggregate temporal features and a loss function. Although many methods on temporal modeling have been proposed, it is hard to directly compare these methods, because the choice of feature extractor and loss function also have a large impact on the final performance. We comprehensively study and compare four different temporal modeling methods (temporal pooling, temporal attention, RNN and 3D convnets) for video-based person reID. We also propose a new attention generation network which adopts temporal convolution to extract temporal information among frames. The evaluation is done on the MARS dataset, and our methods outperform state-of-the-art methods by a large margin. Our source codes are released at this https URL.

Connected through hyperedge HE-1 (induced by source paper "a spectral nonlocal block for neural networks"):
  with members:
    - "compact global descriptor for neural networks"
    - "lightweight network architecture for real time action recognition"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "scan self and collaborative attention network for video person re identification"
    - "a comprehensive survey on graph neural networks"
    - "stochastic trajectory prediction with social graph network"
    - "ccnet criss cross attention for semantic segmentation"
    - "compact generalized non local network"

Connected through hyperedge HE-2 (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"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "learning policies for adaptive tracking with deep feature cascades"

Connected through hyperedge HE-3 (induced by source paper "a similarity measure for material appearance"):
  with members:
    - "learning icons appearance similarity"
    - "on the convergence of adam and beyond"
    - "brdf estimation of complex materials with nested learning"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "recognizing material properties from images"
    - "gaussian material synthesis"
    - "a lightweight approach for on the fly reflectance estimation"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-4 (induced by source paper "rethinking temporal fusion for video based person re identification on semantic and time aspect"):
  with members:
    - "operator in the loop deep sequential multi camera feature fusion for person re identification"
    - "video based person re identification via 3d convolutional networks and non local attention"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "person re identification with fusion of hand crafted and deep pose based body region features"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "intra clip aggregation for video person re identification"):
  with members:
    - "video based person re identification via 3d convolutional networks and non local attention"
    - "spatial temporal synergic residual learning for video person re identification"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "quality aware network for set to set recognition"
    - "jointly attentive spatial temporal pooling networks for video based person re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"

Connected through hyperedge HE-6 (induced by source paper "video person re id fantastic techniques and where to find them"):
  with members:
    - "deep metric learning by online soft mining and class aware attention"
    - "sta spatial temporal attention for large scale video based person re identification"

Connected through hyperedge HE-7 (induced by source paper "video based person re identification via 3d convolutional networks and non local attention"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "part aligned bilinear representations for person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "video based person re identification with accumulative motion context"
    - "person re identification via recurrent feature aggregation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"

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"
    - "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"
    - "region based quality estimation network for large scale 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)