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: "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"
  Abstract: Abstract   The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments.

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 "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"
    - "potential functions based sampling heuristic for optimal path planning"
    - "value iteration networks"

Connected through hyperedge HE-3 (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"
    - "potential functions based sampling heuristic for optimal path planning"
    - "end to end learning for self driving cars"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-4 (induced by source paper "deeply informed neural sampling for robot motion planning"):
  with members:
    - "motion planning networks"
    - "potential functions based sampling heuristic for optimal path planning"
    - "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"
    - "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-5 (induced by source paper "potentially guided bidirectionalized rrt for fast optimal path planning in cluttered environments"):
  with members:
    - "potential functions based sampling heuristic for optimal path planning"
    - "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)