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: "bridging the domain gap for ground to aerial image matching"
  Abstract: The visual entities in cross-view images exhibit drastic domain changes due to the difference in viewpoints each set of images is captured from. Existing state-of-the-art methods address the problem by learning view-invariant descriptors for the images. We propose a novel method for solving this task by exploiting the generative powers of conditional GANs to synthesize an aerial representation of a ground level panorama and use it to minimize the domain gap between the two views. The synthesized image being from the same view as the target image helps the network to preserve important cues in aerial images following our Joint Feature Learning approach. Our Feature Fusion method combines the complementary features from a synthesized aerial image with the corresponding ground features to obtain a robust query representation. In addition, multi-scale feature aggregation preserves image representations at different feature scales useful for solving this complex task. Experimental results show that our proposed approach performs significantly better than the state-of-the-art methods on the challenging CVUSA dataset in terms of top-1 and top-1% retrieval accuracies. Furthermore, to evaluate the generalization of our method on urban landscapes, we collected a new cross-view localization dataset with geo-reference information.

Connected through hyperedge HE-1 (induced by source paper "evaluation of cross view matching to improve ground vehicle localization with aerial perception"):
  with members:
    - "leveraging photogrammetric mesh models for aerial ground feature point matching toward integrated 3d reconstruction"
    - "lending orientation to neural networks for cross view geo localization"
    - "project autovision localization and 3d scene perception for an autonomous vehicle with a multi camera system"
    - "cross view image matching for geo localization in urban environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "semantic cross view matching"
    - "wide area image geolocalization with aerial reference imagery"
    - "faster r cnn towards real time object detection with region proposal 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"
    - "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"
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"

Connected through hyperedge HE-3 (induced by source paper "cross view image synthesis using geometry guided conditional gans"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "pros and cons of gan evaluation measures"
    - "dual discriminator generative adversarial nets"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "geometry guided adversarial facial expression synthesis"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "learning to map vehicles into bird s eye view"

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)