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: "fast reinforcement learning with large action sets using error correcting output codes for mdp factorization"
  Abstract: The use of Reinforcement Learning in real-world scenarios is strongly limited by issues of scale. Most RL learning algorithms are unable to deal with problems composed of hundreds or sometimes even dozens of possible actions, and therefore cannot be applied to many real-world problems. We consider the RL problem in the supervised classification framework where the optimal policy is obtained through a multiclass classifier, the set of classes being the set of actions of the problem. We introduce error-correcting output codes (ECOCs) in this setting and propose two new methods for reducing complexity when using rollouts-based approaches. The first method consists in using an ECOC-based classifier as the multiclass classifier, reducing the learning complexity from O(A2) to O(Alog(A)). We then propose a novel method that profits from the ECOC's coding dictionary to split the initial MDP into O(log(A)) seperate two-action MDPs. This second method reduces learning complexity even further, from O(A2) to O(log(A)), thus rendering problems with large action sets tractable. We finish by experimentally demonstrating the advantages of our approach on a set of benchmark problems, both in speed and performance.

Connected through hyperedge HE-1 (induced by source paper "toybox a suite of environments for experimental evaluation of deep reinforcement learning"):
  with members:
    - "let s play again variability of deep reinforcement learning agents in atari environments"
    - "modularization of end to end learning case study in arcade games"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "exploration by random network distillation"
    - "strategic object oriented reinforcement learning"
    - "natural environment benchmarks for reinforcement learning"
    - "investigating human priors for playing video games"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "neural architecture search in embedding space"):
  with members:
    - "sharpdarts faster and more accurate differentiable architecture search"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "don t decay the learning rate increase the batch size"
    - "practical block wise neural network architecture generation"
    - "smash one shot model architecture search through hypernetworks"

Connected through hyperedge HE-3 (induced by source paper "i m sorry dave i m afraid i can t do that deep q learning from forbidden action"):
  with members:
    - "learning action transferable policy with action embedding"
    - "learning action representations for reinforcement learning"
    - "ledeepchef deep reinforcement learning agent for families of text based games"
    - "the natural language of actions"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "textworld a learning environment for text based games"
    - "observe and look further achieving consistent performance on atari"
    - "learn what not to learn action elimination with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "approximate dynamic programming with neural networks in linear discrete action spaces"):
  with members:
    - "the power of deeper networks for expressing natural functions"
    - "deep networks with stochastic depth"
    - "deep learning in neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-5 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning with function valued action spaces for partial differential equation control"):
  with members:
    - "modeling information diffusion in online social networks with partial differential equations"
    - "lipschitz continuity in model based reinforcement learning"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "lipschitz properties for deep convolutional networks"
    - "guided policy search as approximate mirror descent"
    - "deep reinforcement learning with attention for slate markov decision processes with high dimensional states and actions"
    - "high dimensional continuous control using generalized advantage estimation"
    - "deep reinforcement learning in large discrete action spaces"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning in large discrete action spaces"):
  with members:
    - "deep reinforcement learning with attention for slate markov decision processes with high dimensional states and actions"
    - "deep reinforcement learning with a natural language action space"
    - "continuous control with deep reinforcement learning"
    - "solving multiclass learning problems via error correcting output codes"

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)