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: "deep reinforcement learning for six degree of freedom planetary powered descent and landing"
  Abstract: Future Mars missions will require advanced guidance, navigation, and control algorithms for the powered descent phase to target specific surface locations and achieve pinpoint accuracy (landing error ellipse $<$ 5 m radius). The latter requires both a navigation system capable of estimating the lander's state in real-time and a guidance and control system that can map the estimated lander state to a commanded thrust for each lander engine. In this paper, we present a novel integrated guidance and control algorithm designed by applying the principles of reinforcement learning theory. The latter is used to learn a policy mapping the lander's estimated state directly to a commanded thrust for each engine, with the policy resulting in accurate and fuel-efficient trajectories. Specifically, we use proximal policy optimization, a policy gradient method, to learn the policy. Another contribution of this paper is the use of different discount rates for terminal and shaping rewards, which significantly enhances optimization performance. We present simulation results demonstrating the guidance and control system's performance in a 6-DOF simulation environment and demonstrate robustness to noise and system parameter uncertainty.

Connected through hyperedge HE-1 (induced by source paper "adaptive guidance and integrated navigation with reinforcement meta learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta learning shared hierarchies"
    - "a simple neural attentive meta learner"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning for angle only intercept guidance of maneuvering targets"):
  with members:
    - "adaptive guidance with reinforcement meta learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning accurate extended horizon predictions of high dimensional trajectories"):
  with members:
    - "model based value estimation for efficient model free reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "proximal policy optimization algorithms"
    - "deep visual foresight for planning robot motion"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "adaptive guidance with reinforcement meta learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "gated feedback recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a survey on artificial intelligence trends in spacecraft guidance dynamics and control"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "on the stability analysis of deep neural network representations of an optimal state feedback"
    - "multi rendezvous spacecraft trajectory optimization with beam p aco"
    - "real time optimal control via deep neural networks study on landing problems"
    - "deep learning in neural networks"
    - "mga trajectory planning with an aco inspired algorithm"
    - "an inflationary differential evolution algorithm for space trajectory 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)