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: "deep hybrid similarity learning for person re identification"
  Abstract: Person Re-IDentification (Re-ID) aims to match person images captured from two non-overlapping cameras. In this paper, a deep hybrid similarity learning (DHSL) method for person Re-ID based on a convolution neural network (CNN) is proposed. In our approach, a CNN learning feature pair for the input image pair is simultaneously extracted. Then, both the element-wise absolute difference and multiplication of the CNN learning feature pair are calculated. Finally, a hybrid similarity function is designed to measure the similarity between the feature pair, which is realized by learning a group of weight coefficients to project the element-wise absolute difference and multiplication into a similarity score. Consequently, the proposed DHSL method is able to reasonably assign parameters of feature learning and metric learning in a CNN so that the performance of person Re-ID is improved. Experiments on three challenging person Re-ID databases, QMUL GRID, VIPeR and CUHK03, illustrate that the proposed DHSL method is superior to multiple state-of-the-art person Re-ID methods.

Connected through hyperedge HE-1 (induced by source paper "person re identification by semantic region representation and topology constraint"):
  with members:
    - "harmonious attention network for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "camera style adaptation for person re identification"
    - "part based deep hashing for large scale person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "pose invariant embedding for deep person re identification"

Connected through hyperedge HE-2 (induced by source paper "adversarial open world person re identification"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "camera style adaptation for person re identification"
    - "image image domain adaptation with preserved self similarity and domain dissimilarity for person re identification"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "personnet person re identification with deep convolutional neural networks"
    - "an enhanced deep feature representation for person re identification"

Connected through hyperedge HE-3 (induced by source paper "vehicle re identification using quadruple directional deep learning features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "learning a discriminative null space for person re identification"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "a large scale car dataset for fine grained categorization and verification"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "survey on deep learning techniques for person re identification task"):
  with members:
    - "deep feature learning via structured graph laplacian embedding for person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "deep person learning discriminative deep features for 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"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "personnet person re identification with deep convolutional 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)