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: "learning based proxy collision detection for robot motion planning applications"
  Abstract: This paper demonstrates that collision detection-intensive applications such as robotic motion planning may be accelerated by performing collision checks with a machine learning model. We propose Fastron, a learning-based algorithm to model a robot's configuration space to be used as a proxy collision detector in place of standard geometric collision checkers. We demonstrate that leveraging the proxy collision detector results in up to an order of magnitude faster performance in robot simulation and planning than state-of-the-art collision detection libraries. Our results show that Fastron learns a model more than 100 times faster than a competing C-space modeling approach, while also providing theoretical guarantees of learning convergence. Using the OMPL motion planning libraries, we were able to generate initial motion plans across all experiments with varying robot and environment complexities. With Fastron, we can repeatedly perform planning from scratch at a 56 Hz rate, showing its application toward autonomous surgical assistance task in shared environments with human-controlled manipulators. All performance gains were achieved despite using only CPU-based calculations, suggesting further computational gains with a GPU approach that can parallelize tensor algebra. Code is available online.

Connected through hyperedge HE-1 (induced by source paper "autonomous navigation in unknown environments using sparse kernel based occupancy mapping"):
  with members:
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "warped gaussian processes occupancy mapping with uncertain inputs"
    - "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 "neural collision clearance estimator for fast robot motion planning"):
  with members:
    - "estimating configuration space belief from collision checks for motion planning"
    - "robot motion planning in learned latent spaces"
    - "fastron an online learning based model and active learning strategy for proxy collision detection"
    - "learning sampling distributions for robot motion planning"
    - "data driven planning via imitation learning"
    - "adam a method for stochastic optimization"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-3 (induced by source paper "forward kinematics kernel for improved proxy collision checking"):
  with members:
    - "an asymptotically optimal sampling based algorithm for bi directional motion planning"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "a configuration space decomposition scheme for learning based collision checking"):
  with members:
    - "chance constrained motion planning for high dimensional robots"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "motion planning networks"):
  with members:
    - "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"
    - "value iteration networks"

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)