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: "end to end comparative attention networks for person re identification"
  Abstract: Person re-identification across disjoint camera views has been widely applied in video surveillance yet it is still a challenging problem. One of the major challenges lies in the lack of spatial and temporal cues, which makes it difficult to deal with large variations of lighting conditions, viewing angles, body poses, and occlusions. Recently, several deep-learning-based person re-identification approaches have been proposed and achieved remarkable performance. However, most of those approaches extract discriminative features from the whole frame at one glimpse without differentiating various parts of the persons to identify. It is essentially important to examine multiple highly discriminative local regions of the person images in details through multiple glimpses for dealing with the large appearance variance. In this paper, we propose a new soft attention-based model,  i.e. , the end-to-end comparative attention network (CAN), specifically tailored for the task of person re-identification. The end-to-end CAN learns to selectively focus on parts of pairs of person images after taking a few glimpses of them and adaptively  comparing  their appearance. The CAN model is able to learn which parts of images are relevant for discerning persons and automatically integrates information from different parts to determine whether a pair of images belongs to the same person. In other words, our proposed CAN model simulates the human perception process to verify whether two images are from the same person. Extensive experiments on four benchmark person re-identification data sets, including CUHK01, CHUHK03, Market-1501, and VIPeR, clearly demonstrate that our proposed end-to-end CAN for person re-identification outperforms well established baselines significantly and offer the new state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "disassembling the dataset a camera alignment mechanism for multiple tasks in person re identification"):
  with members:
    - "view confusion feature learning for person re identification"
    - "resolution invariant person re identification"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "eanet enhancing alignment for cross domain person re identification"
    - "multi level factorisation net for person re identification"
    - "harmonious attention network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised person re identification by deep asymmetric metric embedding"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "began boundary equilibrium generative adversarial networks"
    - "cross view asymmetric metric learning for unsupervised person re identification"
    - "transferring a semantic representation for person re identification and search"
    - "unsupervised person re identification clustering and fine tuning"
    - "person transfer gan to bridge domain gap for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "wasserstein cnn learning invariant features for nir vis face recognition"

Connected through hyperedge HE-3 (induced by source paper "uncertainty aware multi shot knowledge distillation for image based object re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "horizontal pyramid matching for person re identification"
    - "densely semantically aligned person re identification"

Connected through hyperedge HE-4 (induced by source paper "adaptive deep metric embeddings for person re identification under occlusions"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "glad global local alignment descriptor for pedestrian retrieval"
    - "lattice long short term memory for human action recognition"
    - "pose driven deep convolutional model for person re identification"
    - "svdnet for pedestrian retrieval"
    - "learning a discriminative null space for person re identification"

Connected through hyperedge HE-5 (induced by source paper "an implicit attention mechanism for deep learning pedestrian re identification frameworks"):
  with members:
    - "spherereid deep hypersphere manifold embedding for person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "triplet based deep similarity learning for person re identification"
    - "harmonious attention network for person re identification"
    - "data augmentation by pairing samples for images classification"
    - "human semantic parsing for person re identification"
    - "random erasing data augmentation"
    - "a discriminatively learned cnn embedding for person reidentification"

Connected through hyperedge HE-6 (induced by source paper "fmt fusing multi task convolutional neural network for person search"):
  with members:
    - "neural person search machines"
    - "svdnet for pedestrian retrieval"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "joint detection and identification feature learning for person search"
    - "learning to rank in person re identification with metric ensembles"
    - "deep feature learning with relative distance comparison for person re identification"
    - "filtered channel features for pedestrian detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "triplet permutation method for deep learning of single shot person re identification"):
  with members:
    - "fast training of triplet based deep binary embedding networks"
    - "person re identification past present and future"
    - "deep feature learning with relative distance comparison for person re identification"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "cdpm convolutional deformable part models for semantically aligned person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep representation learning with part loss for person re identification"
    - "video person re identification by temporal residual learning"
    - "person re identification with metric learning using privileged information"
    - "multi level factorisation net for person re identification"
    - "end to end deep kronecker product matching for person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "learning view specific deep networks for person re identification"

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)