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: "multi modal sensor registration for vehicle perception via deep neural networks"
  Abstract: The ability to simultaneously leverage multiple modes of sensor information is critical for perception of an automated vehicle's physical surroundings. Spatio-temporal alignment of registration of the incoming information is often a prerequisite to analyzing the fused data. The persistence and reliability of multi-modal registration is therefore the key to the stability of decision support systems ingesting the fused information. LiDAR-video systems like on those many driverless cars are a common example of where keeping the LiDAR and video channels registered to common physical features is important. We develop a deep learning method that takes multiple channels of heterogeneous data, to detect the misalignment of the LiDAR-video inputs. A number of variations were tested on the Ford LiDAR-video driving test data set and will be discussed. To the best of our knowledge the use of multi-modal deep convolutional neural networks for dynamic real-time LiDAR-video registration has not been presented.

Connected through hyperedge HE-1 (induced by source paper "pointfusion deep sensor fusion for 3d bounding box estimation"):
  with members:
    - "focal loss for dense object detection"
    - "sparsity invariant cnns"
    - "3d bounding box estimation using deep learning and geometry"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "subcategory aware convolutional neural networks for object proposals and detection"
    - "3d fully convolutional network for vehicle detection in point cloud"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-2 (induced by source paper "regnet multimodal sensor registration using deep neural networks"):
  with members:
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"
    - "network in network"

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)