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: "perceptual generative adversarial networks for small object detection"
  Abstract: Detecting small objects is notoriously challenging due to their low resolution and noisy representation. Existing object detection pipelines usually detect small objects through learning representations of all the objects at multiple scales. However, the performance gain of such ad hoc architectures is usually limited to pay off the computational cost. In this work, we address the small object detection problem by developing a single architecture that internally lifts representations of small objects to "super-resolved" ones, achieving similar characteristics as large objects and thus more discriminative for detection. For this purpose, we propose a new Perceptual Generative Adversarial Network (Perceptual GAN) model that improves small object detection through narrowing representation difference of small objects from the large ones. Specifically, its generator learns to transfer perceived poor representations of the small objects to super-resolved ones that are similar enough to real large objects to fool a competing discriminator. Meanwhile its discriminator competes with the generator to identify the generated representation and imposes an additional perceptual requirement - generated representations of small objects must be beneficial for detection purpose - on the generator. Extensive evaluations on the challenging Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the superiority of Perceptual GAN in detecting small objects, including traffic signs and pedestrians, over well-established state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "extended feature pyramid network for small object detection"):
  with members:
    - "reppoints point set representation for object detection"
    - "libra r cnn towards balanced learning for object detection"
    - "image super resolution by neural texture transfer"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "improving image autoencoder embeddings with perceptual loss"):
  with members:
    - "autoencoder based articulatory to acoustic mapping for ultrasound silent speech interfaces"
    - "mixmatch a holistic approach to semi supervised learning"
    - "autoaugment learning augmentation policies from data"
    - "recurrent world models facilitate policy evolution"
    - "a pitfall of unsupervised pre training"
    - "beyond the pixel wise loss for topology aware delineation"
    - "perceptual losses for real time style transfer and super resolution"
    - "generating images with perceptual similarity metrics based on deep networks"

Connected through hyperedge HE-3 (induced by source paper "measures to evaluate generative adversarial networks based on direct analysis of generated images"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "pros and cons of gan evaluation measures"
    - "perceptual adversarial networks for image to image transformation"
    - "improved training of wasserstein gans"
    - "gp gan towards realistic high resolution image blending"
    - "dualgan unsupervised dual learning for image to image translation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-4 (induced by source paper "does time delay feedback matter to small target motion detection against complex dynamic environments"):
  with members:
    - "a directionally selective small target motion detecting visual neural network in cluttered backgrounds"
    - "a robust visual system for small target motion detection against cluttered moving backgrounds"
    - "contrast oriented deep neural networks for salient object detection"
    - "a feedback neural network for small target motion detection in cluttered backgrounds"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "human pose estimation with iterative error feedback"

Connected through hyperedge HE-5 (induced by source paper "reshapegan object reshaping by providing a single reference image"):
  with members:
    - "representation learning by rotating your faces"
    - "semantic image synthesis with spatially adaptive normalization"
    - "pose guided human video generation"
    - "the gan landscape losses architectures regularization and normalization"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "generative adversarial networks an overview"

Connected through hyperedge HE-6 (induced by source paper "yes we gan applying adversarial techniques for autonomous driving"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "fast converging conditional generative adversarial networks for image synthesis"
    - "multimodal unsupervised image to image translation"
    - "universal adversarial training"
    - "large scale gan training for high fidelity natural image synthesis"
    - "learning to drive from simulation without real world labels"
    - "point cloud gan"

Connected through hyperedge HE-7 (induced by source paper "machine vision for improved human robot cooperation in adverse underwater conditions"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "real world underwater enhancement challenges benchmarks and solutions"
    - "fast underwater image enhancement for improved visual perception"
    - "robot to robot relative pose estimation using humans as markers"
    - "deep visual attention prediction"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "underwater surveying via bearing only cooperative localization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "region wise generative adversarial imageinpainting for large missing areas"):
  with members:
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "pluralistic image completion"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "image inpainting for irregular holes using partial convolutions"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "free form image inpainting with gated convolution"
    - "an empirical study on evaluation metrics of generative 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)