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 "hats histograms of averaged time surfaces for robust event based object classification"):
  Abstract: Event-based cameras have recently drawn the attention of the Computer Vision community thanks to their advantages in terms of high temporal resolution, low power consumption and high dynamic range, compared to traditional frame-based cameras. These properties make event-based cameras an ideal choice for autonomous vehicles, robot navigation or UAV vision, among others. However, the accuracy of event-based object classification algorithms, which is of crucial importance for any reliable system working in real-world conditions, is still far behind their frame-based counterparts. Two main reasons for this performance gap are: 1. The lack of effective low-level representations and architectures for event-based object classification and 2. The absence of large real-world event-based datasets. In this paper we address both problems. First, we introduce a novel event-based feature representation together with a new machine learning architecture. Compared to previous approaches, we use local memory units to efficiently leverage past temporal information and build a robust event-based representation. Second, we release the first large real-world event-based dataset for object classification. We compare our method to the state-of-the-art with extensive experiments, showing better classification performance and real-time computation.

Includes members:
  Member "the event camera dataset and simulator event based data for pose estimation visual odometry and slam":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "eventsr from asynchronous events to image reconstruction restoration and super resolution via end to end adversarial learning")
  Member "phased lstm accelerating recurrent network training for long or event based sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "yolo9000 better faster stronger":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "training deep spiking neural networks using backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "hfirst a temporal approach to object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "converting static image datasets to spiking neuromorphic datasets using saccades":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix lstm a differentiable recurrent surface for asynchronous event based data")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)