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: "semi dense visual odometry for rgb d cameras using approximate nearest neighbour fields"
  Abstract: This paper presents a robust and efficient semi-dense visual odometry solution for RGB-D cameras. The core of our method is a 2D-3D ICP pipeline which estimates the pose of the sensor by registering the projection of a 3D semi-dense map of the reference frame with the 2D semi-dense region extracted in the current frame. The processing is speeded up by efficiently implemented approximate nearest neighbour fields under the Euclidean distance criterion, which permits the use of compact Gauss-Newton updates in the optimization. The registration is formulated as a maximum a posterior problem to deal with outliers and sensor noises, and consequently the equivalent weighted least squares problem is solved by iteratively reweighted least squares method. A variety of robust weight functions are tested and the optimum is determined based on the characteristics of the sensor model. Extensive evaluation on publicly available RGB-D datasets shows that the proposed method predominantly outperforms existing state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "robust monocular edge visual odometry through coarse to fine data association"):
  with members:
    - "semantic nearest neighbor fields monocular edge visual odometry"
    - "se slam semi dense structured edge based monocular slam"
    - "ldso direct sparse odometry with loop closure"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "edge slam edge points based monocular visual slam"
    - "direct sparse odometry"
    - "holistically nested edge detection"

Connected through hyperedge HE-2 (induced by source paper "semantic nearest neighbor fields monocular edge visual odometry"):
  with members:
    - "ds slam a semantic visual slam towards dynamic environments"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "simultaneous edge alignment and learning"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "casenet deep category aware semantic edge detection"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "direct sparse odometry"

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)