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: "sta spatial temporal attention for large scale video based person re identification"
  Abstract: In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person re-identification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMC-VideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.

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 "global local temporal representations for video person re identification"):
  with members:
    - "ocnet object context network for scene parsing"
    - "harmonious attention network for person re identification"
    - "ccnet criss cross attention for semantic segmentation"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "self attention grid for person re identification"
    - "part aligned bilinear representations for person re identification"
    - "dual attention network for scene segmentation"
    - "spatial and temporal mutual promotion for video based person re identification"

Connected through hyperedge HE-4 (induced by source paper "video person re identification using learned clip similarity aggregation"):
  with members:
    - "on the convergence of adam and beyond"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "multi scale 3d convolution network for video based person re identification"
    - "part aligned bilinear representations for person re identification"
    - "spatial and temporal mutual promotion 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"

Connected through hyperedge HE-5 (induced by source paper "id aware quality for set based person re identification"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "multi scale 3d convolution network for video based person re identification"
    - "learn to pay attention"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "video based person re identification with accumulative motion context"
    - "person re identification via recurrent feature aggregation"
    - "re ranking person re identification with k reciprocal encoding"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-6 (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"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "revisiting temporal modeling for video based person reid"
    - "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-7 (induced by source paper "spatially and temporally efficient non local attention network for video based person re identification"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "in defense of the triplet loss for person re identification"
    - "non local neural networks"
    - "sphereface deep hypersphere embedding for face recognition"
    - "attention is all you need"
    - "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"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-8 (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"
    - "revisiting temporal modeling for video based person reid"

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)