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: "safe motion planning in unknown environments optimality benchmarks and tractable policies"
  Abstract: This paper addresses the problem of planning a safe (i.e., collision-free) trajectory from an initial state to a goal region when the obstacle space is a-priori unknown and is incrementally revealed online, e.g., through line-of-sight perception. Despite its ubiquitous nature, this formulation of motion planning has received relatively little theoretical investigation, as opposed to the setup where the environment is assumed known. A fundamental challenge is that, unlike motion planning with known obstacles, it is not even clear what an optimal policy to strive for is. Our contribution is threefold. First, we present a notion of optimality for safe planning in unknown environments in the spirit of comparative (as opposed to competitive) analysis, with the goal of obtaining a benchmark that is, at least conceptually, attainable. Second, by leveraging this theoretical benchmark, we derive a pseudo-optimal class of policies that can seamlessly incorporate any amount of prior or learned information while still guaranteeing the robot never collides. Finally, we demonstrate the practicality of our algorithmic approach in numerical experiments using a range of environment types and dynamics, including a comparison with a state of the art method. A key aspect of our framework is that it automatically and implicitly weighs exploration versus exploitation in a way that is optimal with respect to the information available.

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"
    - "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 "an efficient reachability based framework for provably safe autonomous navigation in unknown environments"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "safe and complete real time planning and exploration in unknown environments"
    - "a lyapunov based approach to safe reinforcement learning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "provably safe and robust drone routing via sequential path planning a case study in san francisco and the bay area"
    - "hamilton jacobi reachability a brief overview and recent advances"

Connected through hyperedge HE-3 (induced by source paper "a hamilton jacobi reachability based framework for predicting and analyzing human motion for safe planning"):
  with members:
    - "human motion trajectory prediction a survey"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "multimodal probabilistic model based planning for human robot interaction"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "forecasting interactive dynamics of pedestrians with fictitious play"
    - "finding approximate pomdp solutions through belief compression"

Connected through hyperedge HE-4 (induced by source paper "map predictive motion planning in unknown environments"):
  with members:
    - "conditional neural processes"
    - "generative image inpainting with contextual attention"
    - "cognitive mapping and planning for visual navigation"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-5 (induced by source paper "look before you sweep visibility aware motion planning"):
  with members:
    - "provably safe robot navigation with obstacle uncertainty"
    - "planning for robotic exploration based on forward simulation"

Connected through hyperedge HE-6 (induced by source paper "safely probabilistically complete real time planning and exploration in unknown environments"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "funnel libraries for real time robust feedback motion planning"
    - "learning model predictive control for iterative tasks a data driven control framework"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

Connected through hyperedge HE-7 (induced by source paper "the critical radius in sampling based motion planning"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "probabilistic completeness of rrt for geometric and kinodynamic planning with forward propagation"
    - "scalable asymptotically optimal multi robot motion planning"
    - "minimizing task space frechet error via efficient incremental graph search"
    - "new perspective on sampling based motion planning via random geometric graphs"
    - "collision detection or nearest neighbor search on the computational bottleneck in sampling based motion planning"
    - "deterministic sampling based motion planning optimality complexity and performance"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"

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)