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: "attribute aware attention model for fine grained representation learning"
  Abstract: How to learn a discriminative fine-grained representation is a key point in many computer vision applications, such as person re-identification, fine-grained classification, fine-grained image retrieval, etc. Most of the previous methods focus on learning metrics or ensemble to derive better global representation, which are usually lack of local information. Based on the considerations above, we propose a novel Attribute-Aware Attention Model ($A^3M$), which can learn local attribute representation and global category representation simultaneously in an end-to-end manner. The proposed model contains two attention models: attribute-guided attention module uses attribute information to help select category features in different regions, at the same time, category-guided attention module selects local features of different attributes with the help of category cues. Through this attribute-category reciprocal process, local and global features benefit from each other. Finally, the resulting feature contains more intrinsic information for image recognition instead of the noisy and irrelevant features. Extensive experiments conducted on Market-1501, CompCars, CUB-200-2011 and CARS196 demonstrate the effectiveness of our $A^3M$. Code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "ghostnet more features from cheap operations"):
  with members:
    - "learning instance wise sparsity for accelerating deep models"
    - "autogan neural architecture search for generative adversarial networks"
    - "all you need is a few shifts designing efficient convolutional neural networks for image classification"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "fasterseg searching for faster real time semantic segmentation"
    - "searching for mobilenetv3"
    - "data free learning of student networks"
    - "importance estimation for neural network pruning"

Connected through hyperedge HE-2 (induced by source paper "abd net attentive but diverse 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"
    - "feature mask network for person re identification"
    - "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"
    - "triplet based deep similarity learning for person re identification"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "progressive cross camera soft label learning for semi supervised person re identification"):
  with members:
    - "camera adversarial transfer for unsupervised person re identification"
    - "imitating targets from all sides an unsupervised transfer learning method for person re identification"
    - "unsupervised tracklet person re identification"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "unsupervised person re identification by soft multilabel learning"
    - "invariance matters exemplar memory for domain adaptive person re identification"
    - "label propagation for deep semi supervised learning"
    - "deep adaptive feature embedding with local sample distributions for person re identification"

Connected through hyperedge HE-4 (induced by source paper "beyond human parts dual part aligned representations for person re identification"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "aanet attribute attention network for person re identifications"
    - "object contextual representations for semantic segmentation"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "improving person re identification by attribute and identity 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-5 (induced by source paper "attkgcn attribute knowledge graph convolutional network for person re identification"):
  with members:
    - "learning context graph for person search"
    - "aanet attribute attention network for person re identifications"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "greyreid a two stream deep framework with rgb grey information for person re identification"):
  with members:
    - "unsupervised person image generation with semantic parsing transformation"
    - "aanet attribute attention network for person re identifications"
    - "a novel unsupervised camera aware domain adaptation framework for person re identification"
    - "ca3net contextual attentional attribute appearance network for person re identification"
    - "maskreid a mask based deep ranking neural network for person re identification"
    - "re identification with consistent attentive siamese networks"
    - "dual attention matching network for context aware feature sequence based person re identification"
    - "human semantic parsing for person re identification"

Connected through hyperedge HE-7 (induced by source paper "distraction aware feature learning for human attribute recognition via coarse to fine attention mechanism"):
  with members:
    - "dynamic curriculum learning for imbalanced data classification"
    - "attribute aware pooling for pedestrian attribute recognition"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "localization guided learning for pedestrian attribute recognition"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "fashion forward forecasting visual style in fashion"

Connected through hyperedge HE-8 (induced by source paper "adaptive semantic visual tree for hierarchical embeddings"):
  with members:
    - "deepfashion2 a versatile benchmark for detection pose estimation segmentation and re identification of clothing images"
    - "video person re identification by temporal residual learning"
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "deep randomized ensembles for metric learning"
    - "improving the annotation of deepfashion images for fine grained attribute recognition"
    - "deep metric learning with hierarchical triplet loss"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "beyond triplet loss a deep quadruplet network 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)