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: "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"
  Abstract: As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such "in-the-tail" data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (around 1000 images). To allow for large-scale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right "priors" or parameters for synthesis: we would like realistic data with poses and object configurations that mimic true Precarious Pedestrians. Inspired by Generative Adversarial Networks (GANs), we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset, which we deem the Adversarial Imposters. We demonstrate that this simple pipeline allows one to synthesize realistic training data by making use of rendering/animation engines within a GAN framework. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Adversarial Imposters can also be used for "in-the-tail" validation at test-time, a notoriously difficult challenge for real-world deployment.

Connected through hyperedge HE-1 (induced by source paper "eurocity persons a novel benchmark for person detection in traffic scenes"):
  with members:
    - "yolov3 an incremental improvement"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "soft nms improving object detection with one line of code"
    - "citypersons a diverse dataset for pedestrian detection"
    - "what can help pedestrian detection"
    - "learning non maximum suppression"
    - "perceptual generative adversarial networks for small object detection"
    - "accurate single stage detector using recurrent rolling convolution"

Connected through hyperedge HE-2 (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"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "shapes and context in the wild image synthesis manipulation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "recycle gan unsupervised video retargeting"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "pixelnn example based image synthesis"

Connected through hyperedge HE-4 (induced by source paper "visual dynamics stochastic future generation via layered cross convolutional networks"):
  with members:
    - "unsupervised learning of latent physical properties using perception prediction networks"
    - "real time user guided image colorization with learned deep priors"
    - "forecasting human dynamics from static images"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "view synthesis by appearance flow"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "dynamic filter networks"

Connected through hyperedge HE-5 (induced by source paper "smart iot cameras for crowd analysis based on augmentation for automatic pedestrian detection simulation and annotation"):
  with members:
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "is faster r cnn doing well for pedestrian detection"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-6 (induced by source paper "image based synthesis for deep 3d human pose estimation"):
  with members:
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning from synthetic humans"
    - "convolutional pose machines"
    - "coarse to fine volumetric prediction for single image 3d human pose"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "structured prediction of 3d human pose with deep neural networks"
    - "3d human pose estimation 2d pose estimation matching"

Connected through hyperedge HE-7 (induced by source paper "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"):
  with members:
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "multimodal unsupervised image to image translation"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "autoaugment learning augmentation policies from data"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-8 (induced by source paper "modeling camera effects to improve visual learning from synthetic data"):
  with members:
    - "failing to learn autonomously identifying perception failures for self driving cars"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "unsupervised domain adaptation for semantic segmentation with gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarially tuned scene generation"
    - "dirty pixels optimizing image classification architectures for raw sensor data"
    - "the cityscapes dataset for semantic urban scene understanding"

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)