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 recognition from adaptive parts"
  Abstract: Previous part-based attribute recognition approaches perform part detection and attribute recognition in separate steps. The parts are not optimized for attribute recognition and therefore could be sub-optimal. We present an end-to-end deep learning approach to overcome the limitation. It generates object parts from key points and perform attribute recognition accordingly, allowing adaptive spatial transform of the parts. Both key point estimation and attribute recognition are learnt jointly in a multi-task setting. Extensive experiments on two datasets verify the efficacy of proposed end-to-end approach.

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"
    - "localization guided learning for pedestrian attribute recognition"
    - "resource aware person re identification across multiple resolutions"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "localization guided learning for pedestrian attribute recognition"):
  with members:
    - "tell me where to look guided attention inference network"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "im2flow motion hallucination from static images for action recognition"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "generative adversarial models for people attribute recognition in surveillance"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "weakly supervised localization using deep feature maps"

Connected through hyperedge HE-4 (induced by source paper "deep view sensitive pedestrian attribute inference in an end to end model"):
  with members:
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "attribute and or grammar for joint parsing of human attributes part and pose"
    - "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"
    - "contextual action recognition with r cnn"
    - "fast r cnn"
    - "learning to recognize pedestrian attribute"

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)