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 "ev segnet semantic segmentation for event based cameras"):
  Abstract: Event cameras, or Dynamic Vision Sensor (DVS), are very promising sensors which have shown several advantages over frame based cameras. However, most recent work on real applications of these cameras is focused on 3D reconstruction and 6-DOF camera tracking. Deep learning based approaches, which are leading the state-of-the-art in visual recognition tasks, could potentially take advantage of the benefits of DVS, but some adaptations are needed still needed in order to effectively work on these cameras. This work introduces a first baseline for semantic segmentation with this kind of data. We build a semantic segmentation CNN based on state-of-the-art techniques which takes event information as the only input. Besides, we propose a novel representation for DVS data that outperforms previously used event representations for related tasks. Since there is no existing labeled dataset for this task, we propose how to automatically generate approximated semantic segmentation labels for some sequences of the DDD17 dataset, which we publish together with the model, and demonstrate they are valid to train a model for DVS data only. We compare our results on semantic segmentation from DVS data with results using corresponding grayscale images, demonstrating how they are complementary and worth combining.

Includes members:
  Member "realtime time synchronized event based stereo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "hats histograms of averaged time surfaces for robust event based object classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "event based vision meets deep learning on steering prediction for self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "ev flownet self supervised optical flow estimation for event based cameras":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "cnn based segmentation of medical imaging data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "low memory neural network training a technical report")

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)