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: "graph correspondence transfer for person re identification"
  Abstract: In this paper, we propose a graph correspondence transfer (GCT) approach for person re-identification. Unlike existing methods, the GCT model formulates person re-identification as an off-line graph matching and on-line correspondence transferring problem. In specific, during training, the GCT model aims to learn off-line a set of correspondence templates from positive training pairs with various pose-pair configurations via patch-wise graph matching. During testing, for each pair of test samples, we select a few training pairs with the most similar pose-pair configurations as references, and transfer the correspondences of these references to test pair for feature distance calculation. The matching score is derived by aggregating distances from different references. For each probe image, the gallery image with the highest matching score is the re-identifying result. Compared to existing algorithms, our GCT can handle spatial misalignment caused by large variations in view angles and human poses owing to the benefits of patch-wise graph matching. Extensive experiments on five benchmarks including VIPeR, Road, PRID450S, 3DPES and CUHK01 evidence the superior performance of GCT model over other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "cross view kernel similarity metric learning using pairwise constraints for person re identification"):
  with members:
    - "multiple kernel fisher discriminant metric learning for person re identification"
    - "person re identification with metric learning using privileged information"
    - "a semi supervised maximum margin metric learning approach for small scale person re identification"
    - "maximum margin metric learning over discriminative nullspace for person re identification"
    - "person re identification in identity regression space"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "pose guided multi granularity attention network for text based person search"):
  with members:
    - "person search with natural language description"
    - "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"
    - "vse improving visual semantic embeddings with hard negatives"
    - "multi context attention for human pose estimation"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "deep attributes driven multi camera person re identification"
    - "structured feature learning for pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"

Connected through hyperedge HE-3 (induced by source paper "vehicle re identification with viewpoint aware metric learning"):
  with members:
    - "ram a region aware deep model for vehicle re identification"
    - "densely semantically aligned 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"
    - "pose driven deep convolutional model for person re identification"
    - "disentangled person image generation"
    - "xception deep learning with depthwise separable convolutions"
    - "hard aware deeply cascaded embedding"

Connected through hyperedge HE-4 (induced by source paper "weighted bilinear coding over salient body parts for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "multi level factorisation net for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "divide and fuse a re ranking approach for person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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)