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: "learning to recognize pedestrian attribute"
  Abstract: Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an alternative approach that exploits the context of neighboring pedestrian images for improved attribute inference compared to the conventional SVM-based method. In addition, we conduct extensive experiments to evaluate the informativeness of background and foreground features for attribute recognition. Experiments are based on our newly released pedestrian attribute dataset, which is by far the largest and most diverse of its kind.

Connected through hyperedge HE-1 (induced by source paper "improved hard example mining by discovering attribute based hard person identity"):
  with members:
    - "fmt fusing multi task convolutional neural network for person search"
    - "pedestrian attribute recognition a survey"
    - "auto reid searching for a part aware convnet for person re identification"
    - "dense feature aggregation and pruning for rgbt tracking"
    - "improving person re identification by attribute and identity learning"
    - "in defense of the triplet loss for person re identification"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "a fast rcnn hard positive generation via adversary for object detection"

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"
    - "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"

Connected through hyperedge HE-3 (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-4 (induced by source paper "attribute recognition by joint recurrent learning of context and correlation"):
  with members:
    - "discovering visual concept structure with sparse and incomplete tags"
    - "class rectification hard mining for imbalanced deep learning"
    - "transferring a semantic representation for person re identification and search"
    - "deep attributes driven multi camera person re identification"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "semantic regularisation for recurrent image annotation"
    - "multi task curriculum transfer deep learning of clothing attributes"
    - "cnn rnn a unified framework for multi label image classification"

Connected through hyperedge HE-5 (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"
    - "weakly supervised learning of mid level features for pedestrian attribute recognition and localization"
    - "contextual action recognition with r cnn"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial models for people attribute recognition in surveillance"):
  with members:
    - "photo realistic single image super resolution using a generative adversarial network"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "a richly annotated dataset for pedestrian attribute recognition"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "contextual action recognition with r cnn"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "deep generative image models using a laplacian pyramid of adversarial networks"

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)