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 with attention for slate markov decision processes with high dimensional states and actions"
  Abstract: Many real-world problems come with action spaces represented as feature vectors. Although high-dimensional control is a largely unsolved problem, there has recently been progress for modest dimensionalities. Here we report on a successful attempt at addressing problems of dimensionality as high as $2000$, of a particular form. Motivated by important applications such as recommendation systems that do not fit the standard reinforcement learning frameworks, we introduce Slate Markov Decision Processes (slate-MDPs). A Slate-MDP is an MDP with a combinatorial action space consisting of slates (tuples) of primitive actions of which one is executed in an underlying MDP. The agent does not control the choice of this executed action and the action might not even be from the slate, e.g., for recommendation systems for which all recommendations can be ignored. We use deep Q-learning based on feature representations of both the state and action to learn the value of whole slates. Unlike existing methods, we optimize for both the combinatorial and sequential aspects of our tasks. The new agent's superiority over agents that either ignore the combinatorial or sequential long-term value aspect is demonstrated on a range of environments with dynamics from a real-world recommendation system. Further, we use deep deterministic policy gradients to learn a policy that for each position of the slate, guides attention towards the part of the action space in which the value is the highest and we only evaluate actions in this area. The attention is used within a sequentially greedy procedure leveraging submodularity. Finally, we show how introducing risk-seeking can dramatically improve the agents performance and ability to discover more far reaching strategies.

Connected through hyperedge HE-1 (induced by source paper "recsim a configurable simulation platform for recommender systems"):
  with members:
    - "reinforcement learning when all actions are not always available"
    - "advantage amplification in slowly evolving latent state environments"
    - "challenges of real world reinforcement learning"
    - "toward simulating environments in reinforcement learning based recommendations"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "natural environment benchmarks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "collaborative filtering with a synthetic feedback loop"):
  with members:
    - "topic guided variational autoencoders for text generation"
    - "improving textual network learning with variational homophilic embeddings"
    - "neurec on nonlinear transformation for personalized ranking"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "wasserstein autoencoders for collaborative filtering"
    - "outer product based neural collaborative filtering"
    - "top k off policy correction for a reinforce recommender system"
    - "zero shot learning via class conditioned deep generative models"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning to optimize long term user engagement in recommender systems"):
  with members:
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "reinforcement learning for online information seeking"
    - "more robust doubly robust off policy evaluation"
    - "deep reinforcement learning for list wise recommendations"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "a neural autoregressive approach to collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for slate based recommender systems a tractable decomposition and practical methodology"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "learning a deep listwise context model for ranking refinement"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "discrete sequential prediction of continuous actions for deep rl"
    - "reinforcement learning based recommender system using biclustering technique"
    - "top k off policy correction for a reinforce recommender system"
    - "seq2slate re ranking and slate optimization with rnns"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for page wise recommendations"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "reinforcement learning based recommender system using biclustering technique"
    - "deep reinforcement learning for list wise recommendations"
    - "reinforcement mechanism design for e commerce"
    - "session based recommendations with recurrent neural networks"
    - "deep reinforcement learning in large discrete action spaces"

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"
    - "high dimensional continuous control using generalized advantage estimation"
    - "deep reinforcement learning in large discrete action spaces"
    - "deep reinforcement learning with a natural language action space"

Connected through hyperedge HE-7 (induced by source paper "recommendations with negative feedback via pairwise deep reinforcement learning"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "deep reinforcement learning for page wise recommendations"
    - "deep reinforcement learning for list wise recommendations"
    - "session based recommendations with recurrent neural networks"
    - "pegasus a policy search method for large mdps and pomdps"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "playing atari with deep reinforcement learning"
    - "off policy actor critic"

Connected through hyperedge HE-8 (induced by source paper "deep reinforcement learning for list wise recommendations"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "reinforcement learning for online information seeking"
    - "continuous control with deep reinforcement learning"
    - "empirical analysis of predictive algorithms for collaborative filtering"
    - "playing atari with deep reinforcement learning"
    - "content based book recommending using learning for text categorization"

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.AI(Artificial Intelligence)