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: "generative adversarial models for people attribute recognition in surveillance"
  Abstract: In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing ...) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80\% of the whole person figure.

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 "can adversarial networks hallucinate occluded people with a plausible aspect"):
  with members:
    - "occluded person re identification"
    - "learning to detect and track visible and occluded body joints in a virtual world"
    - "densepose dense human pose estimation in the wild"
    - "perceptual adversarial networks for image to image transformation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "a generative model of people in clothing"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "multi view image generation from a single view"

Connected through hyperedge HE-3 (induced by source paper "an introduction to person re identification with generative adversarial networks"):
  with members:
    - "can adversarial networks hallucinate occluded people with a plausible aspect"
    - "deep adaptive feature embedding with local sample distributions for person re identification"
    - "identity preserving generative adversarial network for cross domain person re identification"
    - "adversarial open world person re identification"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "random occlusion recovery for person re identification"
    - "person re identification with deep similarity guided graph neural network"
    - "fd gan pose guided feature distilling gan for robust person re identification"

Connected through hyperedge HE-4 (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-5 (induced by source paper "systematic analysis of image generation using gans"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "texturegan controlling deep image synthesis with texture patches"
    - "semantic image synthesis via adversarial learning"
    - "face aging with conditional generative adversarial networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "domain translation with conditional gans from depth to rgb face to face"):
  with members:
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"
    - "from depth data to head pose estimation a siamese approach"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "image restoration using autoencoding priors"
    - "deep head pose estimation from depth data for in car automotive applications"
    - "face from depth for head pose estimation on depth images"
    - "poseidon face from depth for driver pose estimation"
    - "image restoration using convolutional auto encoders with symmetric skip connections"

Connected through hyperedge HE-7 (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"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "attribute recognition from adaptive parts"
    - "weakly supervised localization using deep feature maps"

Connected through hyperedge HE-8 (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"

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)