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: "person re identification via recurrent feature aggregation"
  Abstract: We address the person re-identification problem by effectively exploiting a globally discriminative feature representation from a sequence of tracked human regions/patches. This is in contrast to previous person re-id works, which rely on either single frame based person to person patch matching, or graph based sequence to sequence matching. We show that a progressive/sequential fusion framework based on long short term memory (LSTM) network aggregates the frame-wise human region representation at each time stamp and yields a sequence level human feature representation. Since LSTM nodes can remember and propagate previously accumulated good features and forget newly input inferior ones, even with simple hand-crafted features, the proposed recurrent feature aggregation network (RFA-Net) is effective in generating highly discriminative sequence level human representations. Extensive experimental results on two person re-identification benchmarks demonstrate that the proposed method performs favorably against state-of-the-art person re-identification methods.

Connected through hyperedge HE-1 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks"):
  with members:
    - "deep visual attention prediction"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "videomatch matching based video object segmentation"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-2 (induced by source paper "few shot deep adversarial learning for video based person re identification"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "unsupervised person re identification clustering and fine tuning"

Connected through hyperedge HE-3 (induced by source paper "3 d personvlad learning deep global representations for video based person reidentification"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "what and where to match deep spatially multiplicative integration networks for person re identification"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning context graph for person search"):
  with members:
    - "person re identification with deep similarity guided graph neural network"
    - "person search via a mask guided two stream cnn model"
    - "learning human object interactions by graph parsing neural networks"
    - "video salient object detection via fully convolutional networks"
    - "videos as space time region graphs"
    - "unifying identification and context learning for person recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-6 (induced by source paper "video person re identification by temporal residual learning"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "person re identification by camera correlation aware feature augmentation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "in defense of the triplet loss for person re identification"
    - "quality aware network for set to set recognition"
    - "toward end to end face recognition through alignment learning"
    - "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"

Connected through hyperedge HE-7 (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"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "quality aware network for set to set recognition"

Connected through hyperedge HE-8 (induced by source paper "learning feature aggregation in temporal domain for re identification"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "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"

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)