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 linear discriminant analysis on fisher networks a hybrid architecture for person re identification"
  Abstract: Person re-identification is to seek a correct match for a person of interest across views among a large number of imposters. It typically involves two procedures of non-linear feature extractions against dramatic appearance changes, and subsequent discriminative analysis in order to reduce intra- personal variations while enlarging inter-personal differences. In this paper, we introduce a hybrid architecture which combines Fisher vectors and deep neural networks to learn non-linear representations of person images to a space where data can be linearly separable. We reinforce a Linear Discriminant Analysis (LDA) on top of the deep neural network such that linearly separable latent representations can be learnt in an end-to-end fashion. By optimizing an objective function modified from LDA, the network is enforced to produce feature distributions which have a low variance within the same class and high variance between classes. The objective is essentially derived from the general LDA eigenvalue problem and allows to train the network with stochastic gradient descent and back-propagate LDA gradients to compute the gradients involved in Fisher vector encoding. For evaluation we test our approach on four benchmark data sets in person re-identification (VIPeR [1], CUHK03 [2], CUHK01 [3], and Market1501 [4]). Extensive experiments on these benchmarks show that our model can achieve state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "improving person re identification by attribute and identity learning"):
  with members:
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "attention aware compositional network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "person search with natural language description"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "adversarial attribute image person re identification"

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 "pedestrian alignment network for large scale person re identification"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "deep spatial feature reconstruction for partial person re identification alignment free approach"
    - "in defense of the triplet loss for person re identification"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "divide and fuse a re ranking approach for person re identification"
    - "enhancing person re identification in a self trained subspace"

Connected through hyperedge HE-4 (induced by source paper "unlabeled samples generated by gan improve the person re identification baseline in vitro"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "looking beyond appearances synthetic training data for deep cnns in re identification"
    - "end to end comparative attention networks for person re identification"
    - "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"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-5 (induced by source paper "a discriminatively learned cnn embedding for person reidentification"):
  with members:
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "enhancing person re identification in a self trained subspace"
    - "multichannel kernel canonical correlation analysis for cross view person reidentification"
    - "end to end comparative attention networks for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "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-6 (induced by source paper "re ranking person re identification with k reciprocal encoding"):
  with members:
    - "scalable person re identification on supervised smoothed manifold"
    - "end to end comparative attention networks for person re identification"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "deep attributes driven multi camera person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "scalable metric learning via weighted approximate rank component analysis"
    - "personnet person re identification with deep convolutional neural networks"

Connected through hyperedge HE-7 (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"
    - "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)