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 geometry guided conditional gans"
  Abstract: We address the problem of generating images across two drastically different views, namely ground (street) and aerial (overhead) views. Image synthesis by itself is a very challenging computer vision task and is even more so when generation is conditioned on an image in another view. Due the difference in viewpoints, there is small overlapping field of view and little common content between these two views. Here, we try to preserve the pixel information between the views so that the generated image is a realistic representation of cross view input image. For this, we propose to use homography as a guide to map the images between the views based on the common field of view to preserve the details in the input image. We then use generative adversarial networks to inpaint the missing regions in the transformed image and add realism to it. Our exhaustive evaluation and model comparison demonstrate that utilizing geometry constraints adds fine details to the generated images and can be a better approach for cross view image synthesis than purely pixel based synthesis methods.

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 "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-3 (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"
    - "cross view image synthesis using conditional gans"
    - "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "spatial morphing kernel regression for feature interpolation"
    - "large scale mapping of human activity using geo tagged videos"
    - "began boundary equilibrium generative adversarial networks"
    - "pose guided person image generation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "semantic image synthesis via adversarial learning"
    - "generative face completion"

Connected through hyperedge HE-5 (induced by source paper "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"):
  with members:
    - "deep object tracking on dynamic occupancy grid maps using rnns"
    - "learning to look around objects for top view representations of outdoor scenes"
    - "compositional gan learning conditional image composition"
    - "towards end to end lane detection an instance segmentation approach"
    - "compositional gan learning image conditional binary composition"
    - "generative adversarial frontal view to bird view synthesis"
    - "naturalistic driver intention and path prediction using recurrent neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-6 (induced by source paper "from third person to first person dataset and baselines for synthesis and retrieval"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "identifying first person camera wearers in third person videos"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "detecting engagement in egocentric video"
    - "egocentric meets top view"
    - "generative adversarial text to image synthesis"
    - "semantic image inpainting with deep generative models"

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)