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: "cross view image synthesis using conditional gans"
  Abstract: Learning to generate natural scenes has always been a challenging task in computer vision. It is even more painstaking when the generation is conditioned on images with drastically different views. This is mainly because understanding, corresponding, and transforming appearance and semantic information across the views is not trivial. In this paper, we attempt to solve the novel problem of cross-view image synthesis, aerial to street-view and vice versa, using conditional generative adversarial networks (cGAN). Two new architectures called Crossview Fork (X-Fork) and Crossview Sequential (X-Seq) are proposed to generate scenes with resolutions of 64x64 and 256x256 pixels. X-Fork architecture has a single discriminator and a single generator. The generator hallucinates both the image and its semantic segmentation in the target view. X-Seq architecture utilizes two cGANs. The first one generates the target image which is subsequently fed to the second cGAN for generating its corresponding semantic segmentation map. The feedback from the second cGAN helps the first cGAN generate sharper images. Both of our proposed architectures learn to generate natural images as well as their semantic segmentation maps. The proposed methods show that they are able to capture and maintain the true semantics of objects in source and target views better than the traditional image-to-image translation method which considers only the visual appearance of the scene. Extensive qualitative and quantitative evaluations support the effectiveness of our frameworks, compared to two state of the art methods, for natural scene generation across drastically different views.

Connected through hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "local class specific and global image level generative adversarial networks for semantic guided scene generation"
    - "example guided style consistent image synthesis from semantic labeling"
    - "asymmetric generative adversarial networks for image to image translation"
    - "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "attentiongan unpaired image to image translation using attention guided generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "exocentric to egocentric image generation via parallel generative adversarial network"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "from third person to first person dataset and baselines for synthesis and retrieval"
    - "an improved evaluation framework for generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "cycle in cycle generative adversarial networks for keypoint guided image generation"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "gazecorrection self guided eye manipulation in the wild using self supervised generative adversarial networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cascade attention guided residue learning gan for cross modal translation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "gesturegan for hand gesture to gesture translation in the wild"

Connected through hyperedge HE-4 (induced by source paper "local class specific and global image level generative adversarial networks for semantic guided scene generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "progressive pose attention transfer for person image generation"
    - "a layer based sequential framework for scene generation with gans"
    - "bridging the domain gap for ground to aerial image matching"
    - "singan learning a generative model from a single natural image"
    - "autogan neural architecture search for generative adversarial networks"
    - "coco gan generation by parts via conditional coordinating"
    - "few shot unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "cross view semantic segmentation for sensing surroundings"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "high resolution representations for labeling pixels and regions"
    - "learning exploration policies for navigation"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-6 (induced by source paper "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"):
  with members:
    - "attribute guided sketch generation"
    - "attention guided generative adversarial networks for unsupervised image to image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "soft gated warping gan for pose guided person image synthesis"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "novel view synthesis for large scale scene using adversarial loss"
    - "attention gan for object transfiguration in wild images"
    - "dense pose transfer"

Connected through hyperedge HE-7 (induced by source paper "unified generative adversarial networks for controllable image to image translation"):
  with members:
    - "attribute guided sketch generation"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "bridging the domain gap for ground to aerial image matching"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "from third person to first person dataset and baselines for synthesis and retrieval"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "cross view image synthesis using geometry guided conditional gans"
    - "generative adversarial frontal view to bird view synthesis"
    - "cross view image matching for geo localization in urban environments"
    - "in defense of the triplet loss for person re identification"
    - "learning to discover cross domain relations with 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)