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: "localization guided learning for pedestrian attribute recognition"
  Abstract: Pedestrian attribute recognition has attracted many attentions due to its wide applications in scene understanding and person analysis from surveillance videos. Existing methods try to use additional pose, part or viewpoint information to complement the global feature representation for attribute classification. However, these methods face difficulties in localizing the areas corresponding to different attributes. To address this problem, we propose a novel Localization Guided Network which assigns attribute-specific weights to local features based on the affinity between proposals pre-extracted proposals and attribute locations. The advantage of our model is that our local features are learned automatically for each attribute and emphasized by the interaction with global features. We demonstrate the effectiveness of our Localization Guided Network on two pedestrian attribute benchmarks (PA-100K and RAP). Our result surpasses the previous state-of-the-art in all five metrics on both datasets.

Connected through hyperedge HE-1 (induced by source paper "pedestrian attribute recognition a survey"):
  with members:
    - "the deeper the better analysis of person attributes recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "improving person re identification by attribute and identity learning"
    - "amc automl for model compression and acceleration on mobile devices"
    - "rgb t object tracking benchmark and baseline"
    - "semstyle learning to generate stylised image captions using unaligned text"
    - "describe and attend to track learning natural language guided structural representation and visual attention for object tracking"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (induced by source paper "improving pedestrian attribute recognition with weakly supervised multi scale attribute specific localization"):
  with members:
    - "pedestrian attribute recognition a survey"
    - "harmonious attention network for person re identification"
    - "transferable joint attribute identity deep learning for unsupervised person re identification"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "resource aware person re identification across multiple resolutions"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "squeeze and excitation networks"
    - "hydraplus net attentive deep features for pedestrian analysis"

Connected through hyperedge HE-3 (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"
    - "attribute aware attention model for fine grained representation learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "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-4 (induced by source paper "dynamic curriculum learning for imbalanced data classification"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "imbalanced deep learning by minority class incremental rectification"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "class rectification hard mining for imbalanced deep learning"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"

Connected through hyperedge HE-5 (induced by source paper "attribute aware pooling for pedestrian attribute recognition"):
  with members:
    - "attribute aware attention model for fine grained representation learning"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "autoencoder inspired unsupervised feature selection"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "learning deep feature representations with domain guided dropout for person re identification"

Connected through hyperedge HE-6 (induced by source paper "sequence based person attribute recognition with joint ctc attention model"):
  with members:
    - "multi level factorisation net for person re identification"
    - "deep imbalanced attribute classification using visual attention aggregation"
    - "in defense of the triplet loss for person re identification"
    - "attention is all you need"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "generative adversarial models for people attribute recognition in surveillance"

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)