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: "warped gaussian processes occupancy mapping with uncertain inputs"
  Abstract: In this paper, we study extensions to the Gaussian processes (GPs) continuous occupancy mapping problem. There are two classes of occupancy mapping problems that we particularly investigate. The first problem is related to mapping under pose uncertainty and how to propagate pose estimation uncertainty into the map inference. We develop expected kernel and expected submap notions to deal with uncertain inputs. In the second problem, we account for the complication of the robot's perception noise using warped Gaussian processes (WGPs). This approach allows for non-Gaussian noise in the observation space and captures the possible nonlinearity in that space better than standard GPs. The developed techniques can be applied separately or concurrently to a standard GP occupancy mapping problem. According to our experimental results, although taking into account pose uncertainty leads, as expected, to more uncertain maps, by modeling the nonlinearities present in the observation space WGPs improve the map quality.

Connected through hyperedge HE-1 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"

Connected through hyperedge HE-2 (induced by source paper "stochastic triangular mesh mapping a terrain mapping technique for autonomous mobile robots"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-3 (induced by source paper "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"):
  with members:
    - "gaussian process autonomous mapping and exploration for range sensing mobile robots"
    - "persistent monitoring of events with stochastic arrivals at multiple stations"
    - "sampling based algorithms for optimal motion planning"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-4 (induced by source paper "stochastic triangular mesh mapping"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "expectation propagation for approximate bayesian inference"

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)