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: "planning with pixels in almost real time"
  Abstract: Recently, width-based planning methods have been shown to yield state-of-the-art results in the Atari 2600 video games. For this, the states were associated with the (RAM) memory states of the simulator. In this work, we consider the same planning problem but using the screen instead. By using the same visual inputs, the planning results can be compared with those of humans and learning methods. We show that the planning approach, out of the box and without training, results in scores that compare well with those obtained by humans and learning methods, and moreover, by developing an episodic, rollout version of the IW(k) algorithm, we show that such scores can be obtained in almost real time.

Connected through hyperedge HE-1 (induced by source paper "deep policies for width based planning in pixel domains"):
  with members:
    - "beyond the one step greedy approach in reinforcement learning"
    - "improving width based planning with compact policies"
    - "count based exploration with neural density models"
    - "count based exploration in feature space for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "unifying count based exploration and intrinsic motivation"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "model free model based and general intelligence"):
  with members:
    - "generalized planning non deterministic abstractions and trajectory constraints"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "features projections and representation change for generalized planning"
    - "deep learning a critical appraisal"
    - "learning generalized reactive policies using deep neural networks"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"

Connected through hyperedge HE-3 (induced by source paper "accounting for the neglected dimensions of ai progress"):
  with members:
    - "deep reinforcement learning boosted by external knowledge"
    - "deep learning a critical appraisal"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "in datacenter performance analysis of a tensor processing unit"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "design and analysis of the nips 2016 review process"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-4 (induced by source paper "improving width based planning with compact policies"):
  with members:
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "parameter space noise for exploration"
    - "stein variational policy gradient"
    - "count based exploration in feature space for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "noisy networks for exploration"
    - "deep exploration via bootstrapped dqn"
    - "state of the art control of atari games using shallow reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "taking the scenic route automatic exploration for videogames"):
  with members:
    - "monte carlo tree search for simulation based strategy analysis"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "automatic mapping of nes games with mappy"
    - "automated game design learning"
    - "unifying count based exploration and intrinsic motivation"
    - "illuminating search spaces by mapping elites"

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)