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: "how should a robot assess risk towards an axiomatic theory of risk in robotics"
  Abstract: Endowing robots with the capability of assessing risk and making risk-aware decisions is widely considered a key step toward ensuring safety for robots operating under uncertainty. But, how should a robot quantify risk? A natural and common approach is to consider the framework whereby costs are assigned to stochastic outcomes–an assignment captured by a cost random variable. Quantifying risk then corresponds to evaluating a risk metric, i.e., a mapping from the cost random variable to a real number. Yet, the question of what constitutes a “good” risk metric has received little attention within the robotics community. The goal of this paper is to explore and partially address this question by advocating axioms that risk metrics in robotics applications should satisfy in order to be employed as rational assessments of risk. We provide instantiations of the class of risk metrics that satisfy these axioms (referred to as distortion risk metrics) and also discuss pitfalls of commonly used risk metrics in robotics that do not satisfy these axioms. Our hope is that the ideas presented here will lead to a foundational framework for quantifying risk (and hence safety) in robotics applications.

Connected through hyperedge HE-1 (induced by source paper "learning based distributionally robust motion control with gaussian processes"):
  with members:
    - "wasserstein distributionally robust motion control for collision avoidance using conditional value at risk"
    - "imitating driver behavior with generative adversarial networks"
    - "end to end driving via conditional imitation learning"
    - "a real time model based reinforcement learning architecture for robot control"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "wasserstein distributionally robust motion control for collision avoidance using conditional value at risk"):
  with members:
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "quantile qt opt for risk aware vision based robotic grasping"):
  with members:
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "robust and adaptive planning under model uncertainty"):
  with members:
    - "simultaneous active parameter estimation and control using sampling based bayesian reinforcement learning"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "pomdp lite for robust robot planning under uncertainty"
    - "bayesian reinforcement learning a survey"
    - "exploration vs exploitation vs safety risk averse multi armed bandits"

Connected through hyperedge HE-5 (induced by source paper "implicit quantile networks for distributional reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "efficient exploration with double uncertain value networks"
    - "distributional reinforcement learning with quantile regression"
    - "particle value functions"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "an approximation algorithm for risk averse submodular optimization"):
  with members:
    - "the team surviving orienteers problem routing robots in uncertain environments with survival constraints"
    - "efficient informative sensing using multiple robots"

Connected through hyperedge HE-7 (induced by source paper "risk sensitive inverse reinforcement learning via semi and non parametric methods"):
  with members:
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "risk sensitive and robust decision making a cvar optimization approach"
    - "maximum entropy deep inverse reinforcement learning"
    - "cumulative prospect theory meets reinforcement learning prediction and control"
    - "risk sensitive reinforcement learning"
    - "computational rationalization the inverse equilibrium problem"
    - "continuous inverse optimal control with locally optimal examples"
    - "policy gradients with variance related risk criteria"

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)