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: "multisensor online transfer learning for 3d lidar based human classification with a mobile robot"
  Abstract: Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier, exploiting a so-called trajectory probability. Our framework uses this probability to check whether new detections belongs to a human trajectory, estimated by different sensors and/or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors/detectors used.

Connected through hyperedge HE-1 (induced by source paper "human to robot attention transfer for robot execution failure avoidance using stacked neural networks"):
  with members:
    - "provably safe robot navigation with obstacle uncertainty"
    - "social attention modeling attention in human crowds"
    - "stacked attention networks for image question answering"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "eu long term dataset with multiple sensors for autonomous driving"):
  with members:
    - "scalability in perception for autonomous driving waymo open dataset"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "artificial intelligence for long term robot autonomy a survey"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "segmatch segment based place recognition in 3d point clouds"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "epaner team description paper for world robot challenge 2020"):
  with members:
    - "human motion trajectory prediction a survey"
    - "a fully end to end deep learning approach for real time simultaneous 3d reconstruction and material recognition"
    - "3dof pedestrian trajectory prediction learned from long term autonomous mobile robot deployment data"
    - "robots that can adapt like animals"

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)