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 "unsupervised event based learning of optical flow depth and egomotion"):
  Abstract: In this work, we propose a novel framework for unsupervised learning for event cameras that learns motion information from only the event stream. In particular, we propose an input representation of the events in the form of a discretized volume that maintains the temporal distribution of the events, which we pass through a neural network to predict the motion of the events. This motion is used to attempt to remove any motion blur in the event image. We then propose a loss function applied to the motion compensated event image that measures the motion blur in this image. We train two networks with this framework, one to predict optical flow, and one to predict egomotion and depths, and evaluate these networks on the Multi Vehicle Stereo Event Camera dataset, along with qualitative results from a variety of different scenes.

Includes members:
  Member "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction":
    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 "realtime time synchronized event based stereo":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "event based vision a survey")
  Member "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "event based moving object detection and tracking":
    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 dense optical flow and depth from sparse event data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting event driven cameras for spatio temporal prediction of fast changing trajectories")
  Member "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "unsupervised learning of dense optical flow depth and egomotion from sparse event data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "recurrent neural network for learning densedepth and ego motion from video":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robustness meets deep learning an end to end hybrid pipeline for unsupervised learning of egomotion")

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)