human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "event based 6 dof camera tracking from photometric depth maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "globally optimal contrast maximisation for event based motion estimation")
  Member "independent motion detection with event driven cameras":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "event based vision a survey")
  Member "the event camera dataset and simulator event based data for pose estimation visual odometry and slam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "unsupervised learning of depth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "real time panoramic tracking for event cameras":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "demon depth and motion network for learning monocular stereo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "d3vo deep depth deep pose and deep uncertainty for monocular visual odometry")
  Member "unsupervised monocular depth estimation with left right consistency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

Question: Please tell me which class the source paper 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)