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: "scaling up robust mdps by reinforcement learning"
  Abstract: We consider large-scale Markov decision processes (MDPs) with parameter uncertainty, under the robust MDP paradigm. Previous studies showed that robust MDPs, based on a minimax approach to handle uncertainty, can be solved using dynamic programming for small to medium sized problems. However, due to the "curse of dimensionality", MDPs that model real-life problems are typically prohibitively large for such approaches. In this work we employ a reinforcement learning approach to tackle this planning problem: we develop a robust approximate dynamic programming method based on a projected fixed point equation to approximately solve large scale robust MDPs. We show that the proposed method provably succeeds under certain technical conditions, and demonstrate its effectiveness through simulation of an option pricing problem. To the best of our knowledge, this is the first attempt to scale up the robust MDPs paradigm.

Connected through hyperedge HE-1 (induced by source paper "formal language constraints for markov decision processes"):
  with members:
    - "automata guided reinforcement learning with demonstrations"
    - "logically constrained neural fitted q iteration"
    - "shielded decision making in mdps"
    - "verifiable reinforcement learning via policy extraction"
    - "environment independent task specifications via gltl"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "action robust reinforcement learning and applications in continuous control"):
  with members:
    - "adversarial attacks and defences competition"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "exploration conscious reinforcement learning revisited"
    - "parameter space noise for exploration"
    - "robust adversarial reinforcement learning"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "constrained policy improvement for safe and efficient reinforcement learning"):
  with members:
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "supervised policy update for deep reinforcement learning"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "rainbow combining improvements in deep reinforcement learning"
    - "safe policy improvement with baseline bootstrapping"
    - "deep reinforcement learning that matters"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-4 (induced by source paper "safe option critic learning safety in the option critic architecture"):
  with members:
    - "when waiting is not an option learning options with a deliberation cost"
    - "reinforcement learning with a corrupted reward channel"
    - "inverse reward design"
    - "the option critic architecture"
    - "concrete problems in ai safety"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning to design games strategic environments in deep reinforcement learning"):
  with members:
    - "feudal networks for hierarchical reinforcement learning"
    - "real time bidding by reinforcement learning in display advertising"
    - "playing atari with deep reinforcement learning"
    - "a framework for sequential planning in multi agent settings"
    - "reinforcement learning a survey"

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.LG(Machine Learning)