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: "non iterative slam"
  Abstract: The goal of this paper is to create a new framework for dense SLAM that is light enough for micro-robot systems based on depth camera and inertial sensor. Feature-based and direct methods are two mainstreams in visual SLAM. Both methods minimize photometric or reprojection error by iterative solutions, which are computationally expensive. To overcome this problem, we propose a non-iterative framework to reduce computational requirement. First, the attitude and heading reference system (AHRS) and axonometric projection are utilized to decouple the 6 Degree-of-Freedom (DoF) data, so that point clouds can be matched in independent spaces respectively. Second, based on single key-frame training, the matching process is carried out in frequency domain by Fourier transformation, which provides a closed-form non-iterative solution. In this manner, the time complexity is reduced to O(n log n), where n is the number of matched points in each frame. To the best of our knowledge, this method is the first non-iterative and online trainable approach for data association in visual SLAM. Compared with the state-of-the-arts, it runs at a faster speed and obtains 3-D maps with higher resolution yet still with comparable accuracy.

Connected through hyperedge HE-1 (induced by source paper "intensity scan context coding intensity and geometry relations for loop closure detection"):
  with members:
    - "ldso direct sparse odometry with loop closure"
    - "night to day image translation for retrieval based localization"
    - "local descriptor for robust place recognition using lidar intensity"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "segmatch segment based place recognition in 3d point clouds"

Connected through hyperedge HE-2 (induced by source paper "graph optimization approach to range based localization"):
  with members:
    - "kervolutional neural networks"
    - "kernel cross correlator"
    - "ultra wideband aided fast localization and mapping system"
    - "machine learning in wireless sensor networks algorithms strategies and applications"

Connected through hyperedge HE-3 (induced by source paper "kervolutional neural networks"):
  with members:
    - "decoupled networks"
    - "correlation flow robust optical flow using kernel cross correlators"
    - "kernel cross correlator"
    - "non linear convolution filters for cnn based learning"
    - "ultra wideband aided fast localization and mapping system"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic routing between capsules"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "ultra wideband aided fast localization and mapping system"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "high speed tracking with kernelized correlation filters"
    - "orb slam a versatile and accurate monocular slam system"

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.RO(Robotics)