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: "real time 6dof pose relocalization for event cameras with stacked spatial lstm networks"
  Abstract: We present a new method to relocalize the 6DOF pose of an event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is composed of a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image feature space. We show that the spatial dependency plays an important role in the relocalization task and the SP-LSTM can effectively learn this information. The experimental results on a publicly available dataset show that our approach generalizes well and outperforms recent methods by a substantial margin. Overall, our proposed method reduces by approx. 6 times the position error and 3 times the orientation error compared to the current state of the art. The source code and trained models will be released.

Connected through hyperedge HE-1 (induced by source paper "exploiting event driven cameras for spatio temporal prediction of fast changing trajectories"):
  with members:
    - "continuous time visual inertial odometry for event cameras"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "lstm based network for human gait stability prediction in an intelligent robotic rollator"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "state of the art speech recognition with sequence to sequence models"
    - "ddd17 end to end davis driving dataset"
    - "sequence to sequence video to text"

Connected through hyperedge HE-2 (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-3 (induced by source paper "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"):
  with members:
    - "object captioning and retrieval with natural language"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "learning a bidirectional mapping between human whole body motion and natural language using deep recurrent neural networks"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "affordancenet an end to end deep learning approach for object affordance detection"

Connected through hyperedge HE-4 (induced by source paper "motion equivariance of event based camera data with the temporal normalization transform"):
  with members:
    - "motion equivariant networks for event cameras with the temporal normalization transform"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "unsupervised learning of dense optical flow and depth from sparse event data"
    - "unsupervised learning of dense optical flow depth and egomotion from sparse event data"
    - "ev segnet semantic segmentation for event based cameras"
    - "asynchronous photometric feature tracking using events and frames"
    - "event based vision meets deep learning on steering prediction for self driving cars"

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)