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 "energy efficient path planning for ground robots by combining air and ground measurements"):
  Abstract: As mobile robots find increasing use in outdoor applications, designing energy-efficient robot navigation algorithms is gaining importance. There are two primary approaches to energy efficient navigation: Offline approaches rely on a previously built energy map as input to a path planner. Obtaining energy maps for large environments is challenging. Alternatively, the robot can navigate in an online fashion and build the map as it navigates. Online navigation in unknown environments with only local information is still a challenging research problem. In this paper, we present a novel approach which addresses both of these challenges. Our approach starts with a segmented aerial image of the environment. We show that a coarse energy map can be built from the segmentation. However, the absolute energy value for a specific terrain type (e.g. grass) can vary across environments. Therefore, rather than using this energy map directly, we use it to build the covariance function for a Gaussian Process (GP) based representation of the environment. In the online phase, energy measurements collected during navigation are used for estimating energy profiles across the environment using GP regression. Coupled with an A*-like navigation algorithm, we show in simulations that our approach outperforms representative baseline approaches. We also present results from field experiments which demonstrate the practical applicability of our method.

Includes members:
  Member "estimating achievable range of ground robots operating on single battery discharge for operational efficacy amelioration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "orange operational range estimation for mobile robot exploration on a single discharge cycle")
  Member "learning ground traversability from simulations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepsemantichppc hypothesis based planning over uncertain semantic point clouds")
  Member "a survey of motion planning and control techniques for self driving urban vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "efficient informative sensing using multiple robots":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning q network for active information acquisition")

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)