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: "specular to diffuse translation for multi view reconstruction"
  Abstract: Most multi-view 3D reconstruction algorithms, especially when shape-from-shading cues are used, assume that object appearance is predominantly diffuse. To alleviate this restriction, we introduce S2Dnet, a generative adversarial network for transferring multiple views of objects with specular reflection into diffuse ones, so that multi-view reconstruction methods can be applied more effectively. Our network extends unsupervised image-to-image translation to multi-view "specular to diffuse" translation. To preserve object appearance across multiple views, we introduce a Multi-View Coherence loss (MVC) that evaluates the similarity and faithfulness of local patches after the view-transformation. Our MVC loss ensures that the similarity of local correspondences among multi-view images is preserved under the image-to-image translation. As a result, our network yields significantly better results than several single-view baseline techniques. In addition, we carefully design and generate a large synthetic training data set using physically-based rendering. During testing, our network takes only the raw glossy images as input, without extra information such as segmentation masks or lighting estimation. Results demonstrate that multi-view reconstruction can be significantly improved using the images filtered by our network. We also show promising performance on real world training and testing data.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "synthetic data generation and adaption for object detection in smart vending machines"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "carla an open urban driving simulator"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "object detection using deep cnns trained on synthetic images"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-3 (induced by source paper "crossnet latent cross consistency for unpaired image translation"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "nam non adversarial unsupervised domain mapping"
    - "unsupervised attention guided image to image translation"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "temporally coherent general dynamic scene reconstruction"):
  with members:
    - "veritatem dies aperit temporally consistent depth prediction enabled by a multi task geometric and semantic scene understanding approach"
    - "learning 3d shape completion under weak supervision"
    - "temporally coherent 4d reconstruction of complex dynamic scenes"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

Connected through hyperedge HE-5 (induced by source paper "ignor image guided neural object rendering"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "neural rendering and reenactment of human actor videos"
    - "view extrapolation of human body from a single image"
    - "layer structured 3d scene inference via view synthesis"
    - "a variational u net for conditional appearance and shape generation"
    - "stereo magnification learning view synthesis using multiplane images"
    - "everybody dance now"
    - "transformation grounded image generation network for novel 3d view synthesis"

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)