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: "goal driven dynamics learning via bayesian optimization"
  Abstract: Real-world robots are becoming increasingly complex and commonly act in poorly understood environments where it is extremely challenging to model or learn their true dynamics. Therefore, it might be desirable to take a task-specific approach, wherein the focus is on explicitly learning the dynamics model which achieves the best control performance for the task at hand, rather than learning the true dynamics. In this work, we use Bayesian optimization in an active learning framework where a locally linear dynamics model is learned with the intent of maximizing the control performance, and used in conjunction with optimal control schemes to efficiently design a controller for a given task. This model is updated directly based on the performance observed in experiments on the physical system in an iterative manner until a desired performance is achieved. We demonstrate the efficacy of the proposed approach through simulations and real experiments on a quadrotor testbed.

Connected through hyperedge HE-1 (induced by source paper "a new simulation metric to determine safe environments and controllers for systems with unknown dynamics"):
  with members:
    - "soter programming safe robotics system using runtime assurance"
    - "soter a runtime assurance framework for programming safe robotics systems"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "learning quadrotor dynamics using neural network for flight control"
    - "diagnosis and repair for synthesis from signal temporal logic specifications"
    - "towards verified artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "the bottleneck simulator a model based deep reinforcement learning approach"):
  with members:
    - "integrating planning for task completion dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "near optimal behavior via approximate state abstraction"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "deal or no deal end to end learning for negotiation dialogues"
    - "a deep reinforcement learning chatbot"

Connected through hyperedge HE-3 (induced by source paper "temporal difference models model free deep rl for model based control"):
  with members:
    - "discrete sequential prediction of continuous actions for deep rl"
    - "hindsight experience replay"
    - "a distributional perspective on reinforcement learning"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "learning to act by predicting the future"

Connected through hyperedge HE-4 (induced by source paper "imagination augmented agents for deep reinforcement learning"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "value prediction network"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "metacontrol for adaptive imagination based optimization"
    - "recurrent environment simulators"
    - "model based planning with discrete and continuous actions"
    - "virtual vs real trading off simulations and physical experiments in reinforcement learning with bayesian optimization"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-5 (induced by source paper "task based end to end model learning in stochastic optimization"):
  with members:
    - "optnet differentiable optimization as a layer in neural networks"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "input convex neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"
    - "value iteration networks"
    - "multi objective deep reinforcement learning"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-6 (induced by source paper "mbmf model based priors for model free reinforcement learning"):
  with members:
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "task based end to end model learning in stochastic optimization"
    - "continuous deep q learning with model based acceleration"
    - "learning continuous control policies by stochastic value gradients"

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.SY(Systems and Control)