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: "predicting ground level scene layout from aerial imagery"
  Abstract: We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semantic features automatically extracted from co-located ground imagery. Our network architecture takes an aerial image as input, extracts features using a convolutional neural network, and then applies an adaptive transformation to map these features into the ground-level perspective. We use an end-to-end learning approach to minimize the difference between the semantic segmentation extracted directly from the ground image and the semantic segmentation predicted solely based on the aerial image. We show that a model learned using this strategy, with no additional training, is already capable of rough semantic labeling of aerial imagery. Furthermore, we demonstrate that by finetuning this model we can achieve more accurate semantic segmentation than two baseline initialization strategies. We use our network to address the task of estimating the geolocation and geoorientation of a ground image. Finally, we show how features extracted from an aerial image can be used to hallucinate a plausible ground-level panorama.

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 "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "triplet based deep hashing network for cross modal retrieval"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-3 (induced by source paper "geocoding of trees from street addresses and street level images"):
  with members:
    - "simultaneous multi view instance detection with learned geometric soft constraints"
    - "from google maps to a fine grained catalog of street trees"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "automatic discovery and geotagging of objects from street view imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "multi view 3d object detection network for autonomous driving"
    - "faster r cnn towards real time object detection with region proposal networks"

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"
    - "cross view image synthesis using conditional gans"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "visual memory for robust path following"

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"
    - "cross view image synthesis using conditional gans"
    - "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"

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"
    - "cross view image synthesis using conditional gans"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "sketchygan towards diverse and realistic sketch to 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"
    - "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"
    - "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"

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)