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: "aanet attribute attention network for person re identifications"
  Abstract: This paper proposes Attribute Attention Network (AANet), a new architecture that integrates person attributes and attribute attention maps into a classification framework to solve the person re-identification (re-ID) problem. Many person re-ID models typically employ semantic cues such as body parts or human pose to improve the re-ID performance. Attribute information, however, is often not utilized. The proposed AANet leverages on a baseline model that uses body parts and integrates the key attribute information in an unified learning framework. The AANet consists of a global person ID task, a part detection task and a crucial attribute detection task. By estimating the class responses of individual attributes and combining them to form the attribute attention map (AAM), a very strong discriminatory representation is constructed. The proposed AANet outperforms the best state-of-the-art method arXiv:1711.09349v3 [cs.CV] using ResNet-50 by 3.36% in mAP and 3.12% in Rank-1 accuracy on DukeMTMC-reID dataset. On Market1501 dataset, AANet achieves 92.38% mAP and 95.10% Rank-1 accuracy with re-ranking, outperforming arXiv:1804.00216v1 [cs.CV], another state of the art method using ResNet-152, by 1.42% in mAP and 0.47% in Rank-1 accuracy. In addition, AANet can perform person attribute prediction (e.g., gender, hair length, clothing length etc.), and localize the attributes in the query image.

Connected through hyperedge HE-1 (induced by source paper "magnifiernet towards semantic regularization and fusion for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "beyond human parts dual part aligned representations for person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "foreground aware pyramid reconstruction for alignment free occluded person re identification"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "improving person re identification by attribute and identity learning"
    - "learning discriminative features with multiple granularities for person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep attention aware feature learning for person re identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "learning discriminative features with multiple granularities for person re identification"
    - "harmonious attention network for person re identification"
    - "attention aware compositional network for person re identification"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "pyramidal person re identification via multi loss dynamic training"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-3 (induced by source paper "omni scale feature learning for person re identification"):
  with members:
    - "interaction and aggregation network for person re identification"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "torchreid a library for deep learning person re identification in pytorch"
    - "auto reid searching for a part aware convnet for person re identification"
    - "learning generalisable omni scale representations for person re identification"
    - "on the convergence of adam and beyond"
    - "invariance matters exemplar memory for domain adaptive person re identification"

Connected through hyperedge HE-4 (induced by source paper "semantic correlation promoted shape variant context for segmentation"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "towards robust curve text detection with conditional spatial expansion"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "a network structure to explicitly reduce confusion errors in semantic segmentation"
    - "a classification refinement strategy for semantic segmentation"
    - "learning markov clustering networks for scene text detection"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised person re id differentiable graphical learning and a new benchmark"):
  with members:
    - "unsupervised person re identification by deep asymmetric metric embedding"
    - "joint discriminative and generative learning for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "adaptively connected neural networks"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "discovering underlying person structure pattern with relative local distance for person re identification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "person re identification with deep similarity guided graph neural network"

Connected through hyperedge HE-6 (induced by source paper "attention deep model with multi scale deep supervision for person re identification"):
  with members:
    - "beyond human parts dual part aligned representations for person re identification"
    - "ranked list loss for deep metric learning"
    - "joint discriminative and generative learning for person re identification"
    - "omni scale feature learning for person re identification"
    - "auto reid searching for a part aware convnet for person re identification"
    - "selective kernel networks"
    - "multi scale body part mask guided attention for person re identification"
    - "mixed high order attention network for person re identification"

Connected through hyperedge HE-7 (induced by source paper "beyond human parts dual part aligned representations for person re identification"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "object contextual representations for semantic segmentation"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"
    - "devil in the details towards accurate single and multiple human parsing"
    - "multi level factorisation net for person re identification"
    - "ocnet object context network for scene parsing"

Connected through hyperedge HE-8 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "multi label image recognition with graph convolutional networks"
    - "attributes aided part detection and refinement for person re identification"
    - "pedestrian re identification based on tree branch network with local and global learning"
    - "hierarchical graph convolutional networks for semi supervised node classification"
    - "perceive where to focus learning visibility aware part level features for partial person re identification"
    - "improving person re identification by attribute and identity learning"
    - "attribute aware attention model for fine grained representation learning"

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)