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: "appearance descriptors for person re identification a comprehensive review"
  Abstract: In video-surveillance, person re- identication is the task of recognising whether an individual has already been observed over a network of cameras. Typically, this is achieved by exploiting the clothing appearance, as classical biometric traits like the face are impractical in real-world video surveil- lance scenarios. Clothing appearance is represented by means of low-level local and/or global features of the image, usually extracted according to some part- based body model to treat dierent body parts (e.g. torso and legs) independently. This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identication. The most relevant techniques are described in detail, and categorised according to the body models and features used. The aim of this work is to provide a structured body of knowledge and a starting point for researchers willing to conduct novel investigations on this challenging topic.

Connected through hyperedge HE-1 (induced by source paper "chd consecutive horizontal dropout for human gait feature extraction"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "horizontal pyramid matching for person re identification"
    - "gaitset regarding gait as a set for cross view gait recognition"
    - "in defense of the triplet loss for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "improved dropout for shallow and deep learning"
    - "towards dropout training for convolutional neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "real time multiple people tracking with deeply learned candidate selection and person re identification"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "mot16 a benchmark for multi object tracking"
    - "performance measures and a data set for multi target multi camera tracking"
    - "r fcn object detection via region based fully convolutional networks"
    - "multi person tracking by multicut and deep matching"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "rank persistence assessing the temporal performance of real world person re identification"):
  with members:
    - "temporal model adaptation for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "person re identification past present and future"
    - "human in the loop person re identification"
    - "person re identification by discriminative selection in video ranking"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a systematic evaluation and benchmark for person re identification features metrics and datasets"):
  with members:
    - "learning compositional visual concepts with mutual consistency"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "transferring a semantic representation for person re identification and search"
    - "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"
    - "recurrent attention models for depth based person identification"

Connected through hyperedge HE-6 (induced by source paper "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"):
  with members:
    - "learning compositional visual concepts with mutual consistency"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "rank persistence assessing the temporal performance of real world person re identification"
    - "learning a discriminative null space for person re identification"
    - "densely connected convolutional 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)