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: "qmdp net deep learning for planning under partial observability"
  Abstract: This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture. The QMDP-net is fully differentiable and allows for end-to-end training. We train a QMDP-net on different tasks so that it can generalize to new ones in the parameterized task set and "transfer" to other similar tasks beyond the set. In preliminary experiments, QMDP-net showed strong performance on several robotic tasks in simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, as a result of end-to-end learning.

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 "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"

Connected through hyperedge HE-3 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "reinforcement learning via recurrent convolutional neural networks"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "value iteration networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "benchmarking classic and learned navigation in complex 3d environments"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "group normalization"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "gibson env real world perception for embodied agents"
    - "semi parametric topological memory for navigation"
    - "on evaluation of embodied navigation agents"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "building generalizable agents with a realistic and rich 3d environment"

Connected through hyperedge HE-5 (induced by source paper "combining q learning and search with amortized value estimates"):
  with members:
    - "action guidance with mcts for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "policy gradient search online planning and expert iteration without search trees"
    - "benchmarking model based reinforcement learning"
    - "an investigation of model free planning"
    - "unsupervised visuomotor control through distributional planning networks"
    - "structured agents for physical construction"
    - "universal planning networks"

Connected through hyperedge HE-6 (induced by source paper "differentiable algorithm networks for composable robot learning"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "universal planning networks"
    - "residual policy learning"
    - "learning dexterous in hand manipulation"
    - "mpc inspired neural network policies for sequential decision making"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "differentiable mpc for end to end planning and control"

Connected through hyperedge HE-7 (induced by source paper "inductive bias driven reinforcement learning for efficient schedules in heterogeneous clusters"):
  with members:
    - "learning scheduling algorithms for data processing clusters"
    - "asap accelerated short read alignment on programmable hardware"
    - "relational inductive biases deep learning and graph networks"
    - "deep variational reinforcement learning for pomdps"
    - "on improving deep reinforcement learning for pomdps"
    - "the predictron end to end learning and planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning to plan via neural exploration exploitation trees"):
  with members:
    - "learning to search with mctsnets"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep sequential models for sampling based planning"
    - "gated path planning networks"
    - "learning implicit sampling distributions for motion planning"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning sampling distributions for robot motion planning"

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)