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: "temporal model adaptation for person re identification"
  Abstract: Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%.

Connected through hyperedge HE-1 (induced by source paper "attention deep model with multi scale deep supervision for person re identification"):
  with members:
    - "beyond human parts dual part aligned representations for person re identification"
    - "ranked list loss for deep metric learning"
    - "aanet attribute attention network for person re identifications"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "selective kernel networks"
    - "multi scale body part mask guided attention for person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep representation learning with part loss for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "pose invariant embedding for deep person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "multi scale deep learning architectures for person re identification"
    - "convolutional pose machines"

Connected through hyperedge HE-3 (induced by source paper "person re identification with metric learning using privileged information"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "re ranking person re identification with k reciprocal encoding"
    - "enhancing person re identification in a self trained subspace"
    - "end to end comparative attention networks for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-4 (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"
    - "video based person re identification with accumulative motion context"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an evaluation of deep cnn baselines for scene independent person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "person re identification in identity regression space"):
  with members:
    - "person re identification by deep joint learning of multi loss classification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "highly efficient regression for scalable person re identification"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep ranking for person re identification via joint representation learning"

Connected through hyperedge HE-7 (induced by source paper "maximum margin metric learning over discriminative nullspace for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "embedding deep metric for person re identication a study against large variations"

Connected through hyperedge HE-8 (induced by source paper "reinforced temporal attention and split rate transfer for depth based person re identification"):
  with members:
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "person re identification via recurrent feature aggregation"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "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)