human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "event based camera pose tracking using a generative event model"
  Abstract: Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras. Instead of providing frames of intensity measurements synchronously, at artificially chosen rates, event-based cameras provide information on brightness changes asynchronously, when they occur. Such non-redundant pieces of information are called "events". These sensors overcome some of the limitations of traditional cameras (response time, bandwidth and dynamic range) but require new methods to deal with the data they output. We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.

Connected through hyperedge HE-1 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-2 (induced by source paper "video to events bringing modern computer vision closer to event cameras"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "graph based object classification for neuromorphic vision sensing"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "event based vision a survey"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-3 (induced by source paper "evdodgenet deep dynamic obstacle dodging with event cameras"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "event based vision a survey"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based moving object detection and tracking"
    - "a real time game theoretic planner for autonomous two player drone racing"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"

Connected through hyperedge HE-4 (induced by source paper "asynchronous photometric feature tracking using events and frames"):
  with members:
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "real time intensity image reconstruction for event cameras using manifold regularisation"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"):
  with members:
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "ultimate slam combining events images and imu for robust visual slam in hdr and high speed scenarios"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "real time panoramic tracking for event cameras"

Connected through hyperedge HE-6 (induced by source paper "real time panoramic tracking for event cameras"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "direct sparse odometry"
    - "real time intensity image reconstruction for event cameras using manifold regularisation"

Question: Please tell me which class the center node 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)