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: "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"
  Abstract: State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes. Secondly, attribute labels are predicted by regression of the detection response magnitudes. Finally, the locations and rough shapes of pedestrian attributes can be inferred by performing clustering on a fusion of activation maps of the detection layers, where the fusion weights are estimated as the correlation strengths between each attribute and its relevant mid-level features. Extensive experiments are performed on the two currently largest pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset. Results show that the proposed method has achieved competitive performance on attribute recognition, compared to other state-of-the-art methods. Moreover, the results of attribute localization are visualized to understand the characteristics of the proposed method.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "the deeper the better analysis of person attributes recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "transferring a semantic representation for person re identification and search"
    - "deep view sensitive pedestrian attribute inference in an end to end model"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep imbalanced attribute classification using visual attention aggregation"):
  with members:
    - "deep visual attention prediction"
    - "harmonious attention network for person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "deep imbalanced learning for face recognition and attribute prediction"
    - "adaptive svm learning with privileged information for domain adaptation"
    - "focal loss for dense object detection"
    - "curriculum learning for multi task classification of visual attributes"
    - "order free rnn with visual attention for multi label classification"

Connected through hyperedge HE-4 (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"
    - "attribute recognition from adaptive parts"

Connected through hyperedge HE-5 (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"
    - "localization guided learning for pedestrian attribute recognition"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning a robust representation via a deep network on symmetric positive definite manifolds"):
  with members:
    - "deep manifold to manifold transforming network for action recognition"
    - "deepkspd learning kernel matrix based spd representation for fine grained image recognition"
    - "is second order information helpful for large scale visual recognition"
    - "second order convolutional neural networks"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "exploiting structure sparsity for covariance based visual representation"
    - "deep ten texture encoding network"
    - "learning an invariant hilbert space for domain adaptation"

Connected through hyperedge HE-7 (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"
    - "attribute recognition from adaptive parts"
    - "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"
    - "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)