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: "photorealistic image reconstruction from hybrid intensity and event based sensor"
  Abstract: Event based sensors encode pixel wise contrast changes as positive or negative events at the instant they occur. This paradigm shift away from conventional image sensors provides advantages like low bandwidth requirement, low operating power requirement and low latency. However, in the process of encoding these contrast changes most of the spatial intensity information is lost. Previously there have been attempts at recovering this lost intensity information directly from the event data. Although the results are promising they lack the texture and the consistency of natural videos. We propose to reconstruct photorealistic intensity images by using a low frame rate conventional camera along with the event sensor. The low frame rate conventional camera provides us with texture-rich information, while the event sensor provides us with dense temporal measurements. We combine the strength of both by warping the low-frame rate video to intermediate locations where we only have event sensor data, resulting in high frame rate video. For accomplishing this task, we need the knowledge of scene depth and dense ego-motion estimates. We propose to use an unsupervised, learning free method to estimate depth from low-frame rate images. Unlike previous works, instead of estimating ego-motion directly from events, we propose an autoencoder based model to map events to pseudo-intensity frames. Using pseudo-intensity frames as input, we propose an unsupervised, learning free method adapted from Deep Image Prior to estimate the sensor ego-motion. Finally, we demonstrate photo-realistic reconstructions on a real hybrid sensor (DAVIS) dataset.

Connected through hyperedge HE-1 (induced by source paper "high frame rate video reconstruction based on an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "computing spatial image convolutions for event cameras"
    - "learning to extract a video sequence from a single motion blurred image"
    - "scale recurrent network for deep image deblurring"
    - "phase only image based kernel estimation for single image blind deblurring"
    - "bringing a blurry frame alive at high frame rate with an event camera"
    - "bringing alive blurred moments"

Connected through hyperedge HE-2 (induced by source paper "bringing a blurry frame alive at high frame rate with an event camera"):
  with members:
    - "asynchronous spatial image convolutions for event cameras"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "learning to extract a video sequence from a single motion blurred image"
    - "continuous time intensity estimation using event cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "bringing alive blurred moments"
    - "depth map completion by jointly exploiting blurry color images and sparse depth maps"

Connected through hyperedge HE-3 (induced by source paper "continuous time intensity estimation using event cameras"):
  with members:
    - "semi dense 3d reconstruction with a stereo event camera"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "simultaneous optical flow and segmentation sofas using dynamic vision sensor"
    - "asynchronous photometric feature tracking using events and frames"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "real time intensity image reconstruction for event cameras using manifold regularisation"

Connected through hyperedge HE-4 (induced by source paper "event based high dynamic range image and very high frame rate video generation using conditional generative adversarial networks"):
  with members:
    - "continuous time intensity estimation using event cameras"
    - "ev flownet self supervised optical flow estimation for event based cameras"
    - "ddd17 end to end davis driving dataset"
    - "learning to generate images of outdoor scenes from attributes and semantic layouts"
    - "real time intensity image reconstruction for event cameras using manifold regularisation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-5 (induced by source paper "deep image prior"):
  with members:
    - "latent convolutional models"
    - "phase retrieval for fourier ptychography under varying amount of measurements"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "convolutional dictionary learning via local processing"
    - "benchmarking denoising algorithms with real photographs"
    - "the robust manifold defense adversarial training using generative models"
    - "zero shot super resolution using deep internal learning"
    - "photo realistic single image super resolution using a generative adversarial network"

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)