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: "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
  Abstract: Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL. On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori. However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning. To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions. TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values. We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network. Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree. We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al. 2017) on multiple Atari games. Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fast efficient hyperparameter tuning for policy gradients"):
  with members:
    - "meta gradient reinforcement learning"
    - "bayesian optimization in alphago"
    - "deep variational reinforcement learning for pomdps"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "deep reinforcement learning that matters"
    - "hybrid reward architecture for reinforcement learning"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-4 (induced by source paper "deep residual reinforcement learning"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "world models"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "addressing function approximation error in actor critic methods"
    - "model based value estimation for efficient model free reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on hard exploration for reinforcement learning a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual match based training in pommerman technical report"
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "backplay man muss immer umkehren"
    - "using monte carlo tree search as a demonstrator within asynchronous deep rl"
    - "human level intelligence or animal like abilities"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "risks from learned optimization in advanced machine learning systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "universal planning networks"
    - "scalable agent alignment via reward modeling a research direction"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "learning to reinforcement learn"
    - "learning to learn by gradient descent by gradient descent"
    - "safety verification of deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "universal planning networks"):
  with members:
    - "mpc inspired neural network policies for sequential decision making"
    - "zero shot visual imitation"
    - "hindsight experience replay"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "value prediction network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "emergent complexity via multi agent competition"

Connected through hyperedge HE-8 (induced by source paper "differentiable mpc for end to end planning and control"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "mpc inspired neural network policies for sequential decision making"
    - "zero shot visual imitation"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

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)