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: "curious ilqr resolving uncertainty in model based rl"
  Abstract: Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for learning control. In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty. During trajectory optimization the curious iLQR attempts to minimize both the task-dependent cost and the uncertainty in the dynamics model. We demonstrate the approach on reaching tasks with 7-DoF manipulators in simulation and on a real robot. Our experiments show that MBRL with curious iLQR reaches desired end-effector targets more reliably and with less system rollouts when learning a new task from scratch, and that the learned model generalizes better to new reaching tasks.

Connected through hyperedge HE-1 (induced by source paper "action for better prediction"):
  with members:
    - "self supervised exploration via disagreement"
    - "model based reinforcement learning for atari"
    - "manipulation by feel touch based control with deep predictive models"
    - "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"
    - "robonet large scale multi robot learning"
    - "learning predictive models from observation and interaction"
    - "exploration by random network distillation"
    - "reasoning about physical interactions with object oriented prediction and planning"

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"
    - "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"
    - "learning flexible and reusable locomotion primitives for a microrobot"
    - "sim to real learning agile locomotion for quadruped robots"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "bayesian curiosity for efficient exploration in reinforcement learning"):
  with members:
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "diversity is all you need learning skills without a reward function"
    - "efficient exploration through bayesian deep q networks"
    - "the uncertainty bellman equation and exploration"
    - "surprise based intrinsic motivation for deep reinforcement learning"
    - "curiosity driven exploration by self supervised prediction"
    - "exploration a study of count based exploration for deep reinforcement learning"

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)