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: "potential functions based sampling heuristic for optimal path planning"
  Abstract: Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.

Connected through hyperedge HE-1 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-2 (induced by source paper "fully convolutional search heuristic learning for rapid path planners"):
  with members:
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "learning heuristic search via imitation"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "cnn architectures for large scale audio classification"
    - "value iteration networks"
    - "multi scale context aggregation by dilated convolutions"
    - "maximum entropy deep inverse reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "neural path planning fixed time near optimal path generation via oracle imitation"):
  with members:
    - "motion planning networks"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "value iteration networks"
    - "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"

Connected through hyperedge HE-4 (induced by source paper "motion planning networks"):
  with members:
    - "learning based proxy collision detection for robot motion planning applications"
    - "potentially guided bidirectionalized rrt for fast optimal path planning in cluttered environments"
    - "deeply informed neural sampling for robot motion planning"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "learning sampling distributions for robot motion planning"
    - "end to end learning for self driving cars"
    - "overcoming catastrophic forgetting in neural networks"
    - "value iteration networks"

Connected through hyperedge HE-5 (induced by source paper "deeply informed neural sampling for robot motion planning"):
  with members:
    - "motion planning networks"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "experience based planning with sparse roadmap spanners"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "potentially guided bidirectionalized rrt for fast optimal path planning in cluttered environments"):
  with members:
    - "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"
    - "sampling based algorithms for optimal motion planning"

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)