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 meets image search"
  Abstract: For long time, person re-identification and image search are two separately studied tasks. However, for person re-identification, the effectiveness of local features and the "query-search" mode make it well posed for image search techniques. #R##N#In the light of recent advances in image search, this paper proposes to treat person re-identification as an image search problem. Specifically, this paper claims two major contributions. 1) By designing an unsupervised Bag-of-Words representation, we are devoted to bridging the gap between the two tasks by integrating techniques from image search in person re-identification. We show that our system sets up an effective yet efficient baseline that is amenable to further supervised/unsupervised improvements. 2) We contribute a new high quality dataset which uses DPM detector and includes a number of distractor images. Our dataset reaches closer to realistic settings, and new perspectives are provided. #R##N#Compared with approaches that rely on feature-feature match, our method is faster by over two orders of magnitude. Moreover, on three datasets, we report competitive results compared with the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "text guided person image synthesis"):
  with members:
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "synthesizing images of humans in unseen poses"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "person search with natural language description"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "hornet a hierarchical offshoot recurrent network for improving person re id via image captioning"):
  with members:
    - "feature mask network for person re identification"
    - "improving person re identification by attribute and identity learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "re id done right towards good practices for person re identification"
    - "convolutional image captioning"
    - "person re identification by deep joint learning of multi loss classification"
    - "deep person learning discriminative deep features for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"

Connected through hyperedge HE-3 (induced by source paper "a discriminatively learned cnn embedding for person reidentification"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "enhancing person re identification in a self trained subspace"
    - "multichannel kernel canonical correlation analysis for cross view person reidentification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-4 (induced by source paper "person search with natural language description"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "object detection in videos with tubelet proposal networks"
    - "learning deep representations of fine grained visual descriptions"
    - "object detection from video tubelets with convolutional neural networks"
    - "dualnet domain invariant network for visual question answering"
    - "deep attributes driven multi camera person re identification"
    - "segmentation from natural language expressions"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"

Connected through hyperedge HE-5 (induced by source paper "person re identification via recurrent feature aggregation"):
  with members:
    - "recurrent attention models for depth based person identification"
    - "person re identification by discriminative selection in video ranking"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "caffe convolutional architecture for fast feature embedding"
    - "human re identification by matching compositional template with cluster sampling"
    - "positive semidefinite metric learning using boosting like algorithms"

Connected through hyperedge HE-6 (induced by source paper "learning deep feature representations with domain guided dropout for person re identification"):
  with members:
    - "object detection from video tubelets with convolutional neural networks"
    - "structured feature learning for pose estimation"
    - "learning to rank in person re identification with metric ensembles"
    - "simultaneous deep transfer across domains and tasks"
    - "deep feature learning with relative distance comparison for person re identification"
    - "learning transferable features with deep adaptation networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "orientation driven bag of appearances for person re identification"):
  with members:
    - "person re identification by local maximal occurrence representation and metric learning"

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)