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: "fastron an online learning based model and active learning strategy for proxy collision detection"
  Abstract: We introduce the Fastron, a configuration space (C-space) model to be used as a proxy to kinematic-based collision detection. The Fastron allows iterative updates to account for a changing environment through a combination of a novel formulation of the kernel perceptron learning algorithm and an active learning strategy. Our simulations on a 7 degree-of-freedom arm indicate that proxy collision checks may be performed at least 2 times faster than an efficient polyhedral collision checker and at least 8 times faster than an efficient high-precision collision checker. The Fastron model provides conservative collision status predictions by padding C-space obstacles, and proxy collision checking time does not scale poorly as the number of workspace obstacles increases. All results were achieved without GPU acceleration or parallel computing.

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"
    - "warped gaussian processes occupancy mapping with uncertain inputs"
    - "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:
    - "learning based proxy collision detection for robot motion planning applications"
    - "estimating configuration space belief from collision checks for motion planning"
    - "robot motion planning in learned latent spaces"
    - "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 "neural path planning fixed time near optimal path generation via oracle imitation"):
  with members:
    - "motion planning networks"
    - "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"
    - "intelligent bidirectional rapidly exploring random trees for optimal motion planning in complex cluttered environments"

Connected through hyperedge HE-4 (induced by source paper "learning based proxy collision detection for robot motion planning applications"):
  with members:
    - "motion planning networks"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "planning jerk optimized trajectory with discrete time constraints for redundant robots"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (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"
    - "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)