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: "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
  Abstract: In practice, the parameters of control policies are often tuned manually. This is time-consuming and frustrating. Reinforcement learning is a promising alternative that aims to automate this process, yet often requires too many experiments to be practical. In this paper, we propose a solution to this problem by exploiting prior knowledge from simulations, which are readily available for most robotic platforms. Specifically, we extend Entropy Search, a Bayesian optimization algorithm that maximizes information gain from each experiment, to the case of multiple information sources. The result is a principled way to automatically combine cheap, but inaccurate information from simulations with expensive and accurate physical experiments in a cost-effective manner. We apply the resulting method to a cart-pole system, which confirms that the algorithm can find good control policies with fewer experiments than standard Bayesian optimization on the physical system only.

Connected through hyperedge HE-1 (induced by source paper "cyborg an autonomous cyber operations research gym"):
  with members:
    - "autonomous penetration testing using reinforcement learning"
    - "generalization and regularization in dqn"
    - "quantifying generalization in reinforcement learning"
    - "multi fidelity reinforcement learning with gaussian processes"
    - "deep reinforcement learning with double q learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "simulating cyber attacks for fun and profit"

Connected through hyperedge HE-2 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "characterizing and avoiding negative transfer"
    - "detecting and correcting for label shift with black box predictors"

Connected through hyperedge HE-3 (induced by source paper "neonav improving the generalization of visual navigation via generating next expected observations"):
  with members:
    - "splitnet sim2sim and task2task transfer for embodied visual navigation"
    - "habitat a platform for embodied ai research"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning and querying fast generative models for reinforcement learning"
    - "visual semantic navigation using scene priors"
    - "learning to navigate in cities without a map"

Connected through hyperedge HE-4 (induced by source paper "nimbro robots winning robocup 2018 humanoid adultsize soccer competitions"):
  with members:
    - "grown up nimbro robots winning robocup 2017 humanoid adultsize soccer competitions"
    - "hierarchical and state based architectures for robot behavior planning and control"
    - "fused angles a representation of body orientation for balance"
    - "omnidirectional bipedal walking with direct fused angle feedback mechanisms"
    - "robocup 2016 humanoid teensize winner nimbro robust visual perception and soccer behaviors"
    - "a monocular vision system for playing soccer in low color information environments"
    - "nimbro op2x adult sized open source 3d printed humanoid robot"
    - "combining simulations and real robot experiments for bayesian optimization of bipedal gait stabilization"

Connected through hyperedge HE-5 (induced by source paper "knowledge transfer between robots with similar dynamics for high accuracy impromptu trajectory tracking"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "one shot visual imitation learning via meta learning"
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "multi robot transfer learning a dynamical system perspective"
    - "learning invariant feature spaces to transfer skills with reinforcement learning"
    - "learning transferable policies for monocular reactive mav control"
    - "distributed iterative learning control for a team of quadrotors"
    - "learning modular neural network policies for multi task and multi robot transfer"

Connected through hyperedge HE-6 (induced by source paper "safe exploration for interactive machine learning"):
  with members:
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "efficient and safe exploration in deterministic markov decision processes with unknown transition models"
    - "stagewise safe bayesian optimization with gaussian processes"
    - "on kernelized multi armed bandits"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe exploration in markov decision processes"

Connected through hyperedge HE-7 (induced by source paper "resource aware iot control saving communication through predictive triggering"):
  with members:
    - "feedback control goes wireless guaranteed stability over low power multi hop networks"
    - "distributed event based state estimation for networked systems an lmi approach"
    - "event based state estimation an emulation based approach"
    - "predictive and self triggering for event based state estimation"
    - "sensor scheduling in variance based event triggered estimation with packet drops"
    - "cyber physical control of road freight transport"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-8 (induced by source paper "robust model free reinforcement learning with multi objective bayesian optimization"):
  with members:
    - "data efficient auto tuning with bayesian optimization an industrial control study"
    - "recovering robustness in model free reinforcement learning"
    - "deep kernels for optimizing locomotion controllers"
    - "robust adversarial reinforcement learning"
    - "automatic lqr tuning based on gaussian process global optimization"
    - "safe controller optimization for quadrotors with gaussian processes"

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)