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: "synthetic depth of field with a single camera mobile phone"
  Abstract: We describe a method for synthetically creating a shallow depth-of-field image on a mobile phone. Our system is the basis for "Portrait Mode" on the Google Pixel 2 smartphones.

Connected through hyperedge HE-1 (induced by source paper "3d ken burns effect from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "transformable bottleneck networks"
    - "neural point based graphics"
    - "learning the depths of moving people by watching frozen people"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural rerendering in the wild"

Connected through hyperedge HE-2 (induced by source paper "ai benchmark all about deep learning on smartphones in 2019"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "on device neural net inference with mobile gpus"
    - "semantic image synthesis with spatially adaptive normalization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "ai benchmark running deep neural networks on android smartphones"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-3 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"
    - "intel realsense stereoscopic depth cameras"

Connected through hyperedge HE-4 (induced by source paper "sterefo efficient image refocusing with stereo vision"):
  with members:
    - "real time self adaptive deep stereo"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "deeplens shallow depth of field from a single image"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "the perception distortion tradeoff"

Connected through hyperedge HE-5 (induced by source paper "neural imaging pipelines the scourge or hope of forensics"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "stereoscopic dark flash for low light photography"
    - "cnn based adversarial embedding for image steganography"
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "learning rich features for image manipulation detection"
    - "do gans leave artificial fingerprints"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "content authentication for neural imaging pipelines end to end optimization of photo provenance in complex distribution channels"

Connected through hyperedge HE-6 (induced by source paper "learning single camera depth estimation using dual pixels"):
  with members:
    - "wireless software synchronization of multiple distributed cameras"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "digging into self supervised monocular depth estimation"
    - "a general and adaptive robust loss function"
    - "unsupervised learning of depth and ego motion from video"
    - "aperture supervision for monocular depth estimation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-7 (induced by source paper "using scene graph context to improve image generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "relational inductive biases deep learning and graph networks"
    - "image generation from scene graphs"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural motifs scene graph parsing with global context"
    - "inferring generative model structure with static analysis"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-8 (induced by source paper "single image portrait relighting"):
  with members:
    - "relighting humans occlusion aware inverse rendering for full body human images"
    - "group normalization"
    - "learning to estimate indoor lighting from 3d objects"
    - "learning to predict indoor illumination from a single image"
    - "deep bilateral learning for real time image enhancement"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "deep outdoor illumination estimation"
    - "tensorflow a system for large scale machine learning"

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)