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 flexible and reusable locomotion primitives for a microrobot"
  Abstract: The design of gaits for robot locomotion can be a daunting process, which requires significant expert knowledge and engineering. This process is even more challenging for robots that do not have an accurate physical model, such as compliant or micro-scale robots. Data-driven gait optimization provides an automated alternative to analytical gait design. In this letter, we propose a novel approach to efficiently learn a wide range of locomotion tasks with walking robots. This approach formalizes locomotion as a contextual policy search task to collect data, and subsequently uses that data to learn multiobjective locomotion primitives that can be used for planning. As a proof-of-concept we consider a simulated hexapod modeled after a recently developed microrobot, and we thoroughly evaluate the performance of this microrobot on different tasks and gaits. Our results validate the proposed controller and learning scheme on single and multiobjective locomotion tasks. Moreover, the experimental simulations show that without any prior knowledge about the robot used (e.g., dynamics model), our approach is capable of learning locomotion primitives within 250 trials and subsequently using them to successfully navigate through a maze.

Connected through hyperedge HE-1 (induced by source paper "learning directed locomotion in modular robots with evolvable morphologies"):
  with members:
    - "evolving embodied intelligence from materials to machines"
    - "data efficient learning of morphology and controller for a microrobot"
    - "scalable co optimization of morphology and control in embodied machines"
    - "robots that can adapt like animals"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning generalizable locomotion skills with hierarchical reinforcement learning"):
  with members:
    - "data efficient reinforcement learning for legged robots"
    - "curious ilqr resolving uncertainty in model based rl"
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "bayesian optimization in variational latent spaces with dynamic compression"
    - "learning agile and dynamic motor skills for legged robots"
    - "using deep reinforcement learning to learn high level policies on the atrias biped"
    - "sim to real learning agile locomotion for quadruped robots"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "data efficient learning of morphology and controller for a microrobot"):
  with members:
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "from the lab to the desert fast prototyping and learning of robot locomotion"
    - "funneled bayesian optimization for design tuning and control of autonomous systems"
    - "the cma evolution strategy a tutorial"
    - "robots that can adapt like animals"

Connected through hyperedge HE-4 (induced by source paper "factored contextual policy search with bayesian optimization"):
  with members:
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "hindsight experience replay"
    - "learning parameterized skills"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "developmental bayesian optimization of black box with visual similarity based transfer learning"):
  with members:
    - "comparing task simplifications to learn closed loop object picking using deep reinforcement learning"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "unscented bayesian optimization for safe robot grasping"
    - "symbol emergence in robotics a survey"
    - "robots that can adapt like animals"
    - "3d shapenets a deep representation for volumetric shapes"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "gait learning for soft microrobots controlled by light fields"):
  with members:
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "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)