human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "lidartag a real time fiducial tag using point clouds"):
  Abstract: Image-based fiducial markers are widely used in robotics and computer vision problems such as object tracking in cluttered or textureless environments, camera (and multi-sensor) calibration tasks, or vision-based simultaneous localization and mapping (SLAM). The state-of-the-art fiducial marker detection algorithms rely on consistency of the ambient lighting. This paper introduces LiDARTag, a novel fiducial tag design and detection algorithm suitable for light detection and ranging (LiDAR) point clouds. The proposed tag runs in real-time and can process data faster than the currently available LiDAR sensors frequencies. Due to the nature of the LiDAR's sensor, rapidly changing ambient lighting will not affect detection of a LiDARTag; hence, the proposed fiducial marker can operate in a completely dark environment. In addition, the LiDARTag nicely complements available visual fiducial markers as the tag design is compatible with available techniques, such as AprilTags, allowing for efficient multi-sensor fusion and calibration tasks. The experimental results, verified by a motion capture system, confirm the proposed technique can reliably provide a tag's pose and its unique ID code. All implementations are done in C++ and will be available soon at: this https URL

Includes members:
  Member "contact aided invariant extended kalman filtering for legged robot state estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a code for unscented kalman filtering on manifolds ukf m")
  Member "real time rotation invariant face detection with progressive calibration networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "rotation equivariant vector field networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "voxelnet end to end learning for point cloud based 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "da rnn semantic mapping with data associated recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "harmonic networks deep translation and rotation equivariance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on learning sets of symmetric elements")
  Member "vote3deep fast object detection in 3d point clouds using efficient convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review deep learning on 3d point clouds")
  Member "3d fully convolutional network for vehicle detection in point cloud":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "3d point cloud processing and learning for autonomous driving")

Question: Please tell me which class the source paper 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)