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: "adaptive guidance with reinforcement meta learning"
  Abstract: This paper proposes a novel adaptive guidance system developed using reinforcement meta-learning with a recurrent policy and value function approximator. The use of recurrent network layers allows the deployed policy to adapt real time to environmental forces acting on the agent. We compare the performance of the DR/DV guidance law, an RL agent with a non-recurrent policy, and an RL agent with a recurrent policy in four difficult tasks with unknown but highly variable dynamics. These tasks include a safe Mars landing with random engine failure and a landing on an asteroid with unknown environmental dynamics. We also demonstrate the ability of a recurrent policy to navigate using only Doppler radar altimeter returns, thus integrating guidance and navigation.

Connected through hyperedge HE-1 (induced by source paper "learning to guide guidance law based on deep meta learning and model predictive path integral control"):
  with members:
    - "deep haptic model predictive control for robot assisted dressing"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "learning to learn how to learn self adaptive visual navigation using meta learning"
    - "learning image conditioned dynamics models for control of under actuated legged millirobots"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "control of a quadrotor with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for angle only intercept guidance of maneuvering targets"):
  with members:
    - "deep reinforcement learning for six degree of freedom planetary powered descent and landing"
    - "meta learning shared hierarchies"
    - "a simple neural attentive meta learner"
    - "proximal policy optimization algorithms"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"
    - "epopt learning robust neural network policies using model ensembles"
    - "trust region policy optimization"

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)