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: "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
  Abstract: State-of-the-art pedestrian detection models have achieved great success in many benchmarks. However, these models require lots of annotation information and the labeling process usually takes much time and efforts. In this paper, we propose a method to generate labeled pedestrian data and adapt them to support the training of pedestrian detectors. The proposed framework is built on the Generative Adversarial Network (GAN) with multiple discriminators, trying to synthesize realistic pedestrians and learn the background context simultaneously. To handle the pedestrians of different sizes, we adopt the Spatial Pyramid Pooling (SPP) layer in the discriminator. We conduct experiments on two benchmarks. The results show that our framework can smoothly synthesize pedestrians on background images of variations and different levels of details. To quantitatively evaluate our approach, we add the generated samples into training data of the baseline pedestrian detectors and show the synthetic images are able to improve the detectors' performance.

Connected through hyperedge HE-1 (induced by source paper "pmc gans generating multi scale high quality pedestrian with multimodal cascaded gans"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "unsupervised person image generation with semantic parsing transformation"
    - "learning to generate synthetic data via compositing"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "image super resolution using very deep residual channel attention networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "bagan data augmentation with balancing gan"

Connected through hyperedge HE-2 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"
    - "free form image inpainting with gated convolution"

Connected through hyperedge HE-3 (induced by source paper "multimodal image outpainting with regularized normalized diversification"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "object driven text to image synthesis via adversarial training"
    - "normalized diversification"
    - "putting humans in a scene learning affordance in 3d indoor environments"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "binge watching scaling affordance learning from sitcoms"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "esrgan enhanced super resolution generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "generating 3d people in scenes without people"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning elementary structures for 3d shape generation and matching"
    - "does computer vision matter for action"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "the replica dataset a digital replica of indoor spaces"
    - "holistic scene understanding single view 3d holistic scene parsing and human pose estimation with human object interaction and physical commonsense"
    - "expressive body capture 3d hands face and body from a single image"
    - "imapper interaction guided scene mapping from monocular videos"

Connected through hyperedge HE-5 (induced by source paper "a shape transformation based dataset augmentation framework for pedestrian detection"):
  with members:
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "a kernel theory of modern data augmentation"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "strike with a pose neural networks are easily fooled by strange poses of familiar objects"
    - "learning to generate long term future via hierarchical prediction"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"

Connected through hyperedge HE-6 (induced by source paper "inserting videos into videos"):
  with members:
    - "video to video synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "recycle gan unsupervised video retargeting"
    - "artistic style transfer for videos and spherical images"
    - "yolov3 an incremental improvement"
    - "multimodal unsupervised image to image translation"
    - "context aware synthesis and placement of object instances"
    - "learning hierarchical semantic image manipulation through structured representations"

Connected through hyperedge HE-7 (induced by source paper "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"):
  with members:
    - "infinite brain mr images pggan based data augmentation for tumor detection"
    - "a three player gan generating hard samples to improve classification networks"
    - "red blood cell image generation for data augmentation using conditional generative adversarial networks"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "generative image translation for data augmentation of bone lesion pathology"
    - "towards generative adversarial networks as a new paradigm for radiology education"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-8 (induced by source paper "generative modeling for small data object detection"):
  with members:
    - "inserting videos into videos"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "versatile auxiliary classifier with generative adversarial network vac gan multi class scenarios"
    - "context aware synthesis and placement of object instances"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "unsupervised holistic image generation from key local patches"

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)