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: "open set person re identification"
  Abstract: Person re-identification is becoming a hot research for developing both machine learning algorithms and video surveillance applications. The task of person re-identification is to determine which person in a gallery has the same identity to a probe image. This task basically assumes that the subject of the probe image belongs to the gallery, that is, the gallery contains this person. However, in practical applications such as searching a suspect in a video, this assumption is usually not true. In this paper, we consider the open-set person re-identification problem, which includes two sub-tasks, detection and identification. The detection sub-task is to determine the presence of the probe subject in the gallery, and the identification sub-task is to determine which person in the gallery has the same identity as the accepted probe. We present a database collected from a video surveillance setting of 6 cameras, with 200 persons and 7,413 images segmented. Based on this database, we develop a benchmark protocol for evaluating the performance under the open-set person re-identification scenario. Several popular metric learning algorithms for person re-identification have been evaluated as baselines. From the baseline performance, we observe that the open-set person re-identification problem is still largely unresolved, thus further attention and effort is needed.

Connected through hyperedge HE-1 (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"
    - "temporal model adaptation 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"

Connected through hyperedge HE-2 (induced by source paper "pseudo positive regularization for deep person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "tag relevance fusion for social image retrieval"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "svdnet for pedestrian retrieval"
    - "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-3 (induced by source paper "person re identification past present and future"):
  with members:
    - "end to end comparative attention networks for person re identification"
    - "how far are we from solving pedestrian detection"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "disturblabel regularizing cnn on the loss layer"
    - "a survey on learning to hash"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "deep linear discriminant analysis on fisher networks a hybrid architecture for person re identification"

Connected through hyperedge HE-4 (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-5 (induced by source paper "person re identification in the wild"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "re ranking person re identification with k reciprocal encoding"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "how far are we from solving pedestrian detection"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "person re identification past present and future"
    - "joint detection and identification feature learning for person search"

Connected through hyperedge HE-6 (induced by source paper "scalable metric learning via weighted approximate rank component analysis"):
  with members:
    - "learning to rank in person re identification with metric ensembles"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "metric and kernel learning using a linear transformation"

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)