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: "semantic motion segmentation using dense crf formulation"
  Abstract: While the literature has been fairly dense in the areas of scene understanding and semantic labeling there have been few works that make use of motion cues to embellish semantic performance and vice versa. In this paper, we address the problem of semantic motion segmentation, and show how semantic and motion priors augments performance. We propose an algorithm that jointly infers the semantic class and motion labels of an object. Integrating semantic, geometric and optical flow based constraints into a dense CRF-model we infer both the object class as well as motion class, for each pixel. We found improvement in performance using a fully connected CRF as compared to a standard clique-based CRFs. For inference, we use a Mean Field approximation based algorithm. Our method outperforms recently proposed motion detection algorithms and also improves the semantic labeling compared to the state-of-the-art Automatic Labeling Environment algorithm on the challenging KITTI dataset especially for object classes such as pedestrians and cars that are critical to an outdoor robotic navigation scenario.

Connected through hyperedge HE-1 (induced by source paper "rst modnet real time spatio temporal moving object detection for autonomous driving"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "neurall towards a unified model for visual perception in automated driving"
    - "fusemodnet real time camera and lidar based moving object detection for robust low light autonomous driving"
    - "fisheyemodnet moving object detection on surround view cameras for autonomous driving"
    - "real time dynamic object detection for autonomous driving using prior 3d maps"
    - "rtseg real time semantic segmentation comparative study"
    - "shuffleseg real time semantic segmentation network"

Connected through hyperedge HE-2 (induced by source paper "deep semantic classification for 3d lidar data"):
  with members:
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks"
    - "multi view 3d object detection network for autonomous driving"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "incremental real time multibody vslam with trajectory optimization using stereo camera"):
  with members:
    - "dynamic body vslam with semantic constraints"

Connected through hyperedge HE-4 (induced by source paper "dynamic body vslam with semantic constraints"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "a sparse johnson lindenstrauss transform"

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)