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: "person re identification using visual attention"
  Abstract: Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person's appearance can vary significantly when large variations in view angle, human pose, and illumination are involved. In this paper, we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem. Our model learns to focus selectively on parts of the input image for which the networks' output is most sensitive to and processes them with high resolution while perceiving the surrounding image in low resolution. Extensive comparative evaluations demonstrate that the proposed method outperforms state-of-the-art approaches on the challenging CUHK01, CUHK03, and Market 1501 datasets.

Connected through hyperedge HE-1 (induced by source paper "context aware road user importance estimation icare"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "end to end learning for self driving cars"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "recurrent attentional networks for saliency detection"
    - "multi view 3d object detection network for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "sparse label smoothing regularization for person re identification"):
  with members:
    - "multi pseudo regularized label for generated data in person re identification"
    - "clustering with deep learning taxonomy and new methods"
    - "harmonious attention network for person re identification"
    - "crossing generative adversarial networks for cross view person re identification"
    - "re id done right towards good practices for person re identification"
    - "deep co attention based comparators for relative representation learning in person re identification"
    - "unsupervised person re identification by deep learning tracklet association"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "sharp attention network via adaptive sampling for person re identification"):
  with members:
    - "deep representation learning with part loss for person re identification"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "pose driven deep convolutional model for person re identification"
    - "end to end comparative attention networks for person re identification"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "transparency by design closing the gap between performance and interpretability in visual reasoning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning to count objects in natural images for visual question answering"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "sensor transformation attention networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "human semantic parsing for person re identification"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "part based deep hashing for large scale person re identification"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-6 (induced by source paper "a pose sensitive embedding for person re identification with expanded cross neighborhood re ranking"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "random erasing data augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "pose guided person image generation"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "deep mutual learning"
    - "margin sample mining loss a deep learning based method for person re identification"

Connected through hyperedge HE-7 (induced by source paper "face recognition using scattering convolutional network"):
  with members:
    - "masked signal decomposition using subspace representation and its applications"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "palmprint recognition using deep scattering convolutional network"
    - "medical image classification via svm using lbp features from saliency based folded data"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "a bayesian framework for sparse representation based 3 d human pose estimation"
    - "highly accurate multispectral palmprint recognition using statistical and wavelet features"

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)