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: "video based person re identification with accumulative motion context"
  Abstract: Video based person re-identification plays a central role in realistic security and video surveillance. In this paper we propose a novel Accumulative Motion Context (AMOC) network for addressing this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable and thus motion context can be adapted to complement appearance clues under unfavorable conditions (e.g. occlusions). Extensive experiments are conduced on three public benchmark datasets, i.e., the iLIDS-VID, PRID-2011 and MARS datasets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video based person re-identification, validating our motivation evidently.

Connected through hyperedge HE-1 (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"
    - "sta spatial temporal attention for large scale video 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"

Connected through hyperedge HE-2 (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"
    - "person re identification via recurrent feature aggregation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-3 (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"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "part aligned bilinear representations for person re identification"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "person re identification via recurrent feature aggregation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (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"
    - "sta spatial temporal attention for large scale video based person re identification"
    - "learn to pay attention"
    - "spatial and temporal mutual promotion for video based person re identification"
    - "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-5 (induced by source paper "learning adaptively from the unknown for few example video person re id"):
  with members:
    - "harmonious attention network for person re identification"
    - "attention based few shot person re identification using meta learning"
    - "quality aware network for set to set recognition"
    - "dynamic label graph matching for unsupervised video re identification"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "matching networks for one shot learning"
    - "deep feature learning with relative distance comparison for person re identification"
    - "deep metric learning for practical person re identification"

Connected through hyperedge HE-6 (induced by source paper "part aligned bilinear representations for person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person re identification by camera correlation aware feature augmentation"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"
    - "re ranking person re identification with k reciprocal encoding"

Connected through hyperedge HE-7 (induced by source paper "iqiyi vid a large dataset for multi modal person identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "person search in videos with one portrait through visual and temporal links"
    - "the devil of face recognition is in the noise"
    - "arcface additive angular margin loss for deep face recognition"
    - "pyramidbox a context assisted single shot face detector"
    - "voxceleb2 deep speaker recognition"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"

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

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)