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: "deferred neural rendering image synthesis using neural textures"
  Abstract: The modern computer graphics pipeline can synthesize images at remarkable visual quality; however, it requires well-defined, high-quality 3D content as input. In this work, we explore the use of imperfect 3D content, for instance, obtained from photo-metric reconstructions with noisy and incomplete surface geometry, while still aiming to produce photo-realistic (re-)renderings. To address this challenging problem, we introduce Deferred Neural Rendering, a new paradigm for image synthesis that combines the traditional graphics pipeline with learnable components. Specifically, we propose Neural Textures, which are learned feature maps that are trained as part of the scene capture process. Similar to traditional textures, neural textures are stored as maps on top of 3D mesh proxies; however, the high-dimensional feature maps contain significantly more information, which can be interpreted by our new deferred neural rendering pipeline. Both neural textures and deferred neural renderer are trained end-to-end, enabling us to synthesize photo-realistic images even when the original 3D content was imperfect. In contrast to traditional, black-box 2D generative neural networks, our 3D representation gives us explicit control over the generated output, and allows for a wide range of application domains. For instance, we can synthesize temporally-consistent video re-renderings of recorded 3D scenes as our representation is inherently embedded in 3D space. This way, neural textures can be utilized to coherently re-render or manipulate existing video content in both static and dynamic environments at real-time rates. We show the effectiveness of our approach in several experiments on novel view synthesis, scene editing, and facial reenactment, and compare to state-of-the-art approaches that leverage the standard graphics pipeline as well as conventional generative neural networks.

Connected through hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "celeb df a new dataset for deepfake forensics"

Connected through hyperedge HE-2 (induced by source paper "neural human video rendering joint learning of dynamic textures and rendering to video translation"):
  with members:
    - "textured neural avatars"
    - "neural rendering and reenactment of human actor videos"
    - "liquid warping gan a unified framework for human motion imitation appearance transfer and novel view synthesis"
    - "dense intrinsic appearance flow for human pose transfer"
    - "video to video synthesis"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "synthesizing images of humans in unseen poses"
    - "learning to estimate 3d human pose and shape from a single color image"

Connected through hyperedge HE-3 (induced by source paper "seeing the world in a bag of chips"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "rendering synthetic objects into legacy photographs"
    - "neural point based graphics"
    - "deep sky modeling for single image outdoor lighting estimation"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "inverse path tracing for joint material and lighting estimation"
    - "deeplight learning illumination for unconstrained mobile mixed reality"

Connected through hyperedge HE-4 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "use of a capsule network to detect fake images and videos"
    - "learning rich features for image manipulation detection"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-5 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-6 (induced by source paper "deeperforensics 1 0 a large scale dataset for real world face forgery detection"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-7 (induced by source paper "detecting deepfakes with metric learning"):
  with members:
    - "deepfakes and beyond a survey of face manipulation and fake detection"
    - "media forensics and deepfakes an overview"
    - "fsgan subject agnostic face swapping and reenactment"
    - "faceforensics learning to detect manipulated facial images"
    - "celeb df a new dataset for deepfake forensics"
    - "deep learning for deepfakes creation and detection"
    - "mesonet a compact facial video forgery detection network"
    - "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"

Connected through hyperedge HE-8 (induced by source paper "adversarial texture optimization from rgb d scans"):
  with members:
    - "neural point based graphics"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "robust watertight manifold surface generation method for shapenet models"
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"

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)