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 "end to end learning of representations for asynchronous event based data"):
  Abstract: Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events". They have appealing advantages over frame-based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatiotemporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations through a sequence of differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.

Includes members:
  Member "event based vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  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 "unsupervised event based learning of optical flow depth and egomotion":
    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 "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "hats histograms of averaged time surfaces for robust event based object classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "asynchronous photometric feature tracking using events and frames":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "event based vision meets deep learning on steering prediction for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")

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)