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: "strategic object oriented reinforcement learning"
  Abstract: Humans learn to play video games significantly faster than state-of-the-art reinforcement learning (RL) algorithms. Inspired by this, we introduce strategic object oriented reinforcement learning (SOORL) to learn simple dynamics model through automatic model selection and perform efficient planning with strategic exploration. We compare different exploration strategies in a model-based setting in which exact planning is impossible. Additionally, we test our approach on perhaps the hardest Atari game Pitfall! and achieve significantly improved exploration and performance over prior methods.

Connected through hyperedge HE-1 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"

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"
    - "natural environment benchmarks for reinforcement learning"
    - "investigating human priors for playing video games"
    - "quantifying generalization in reinforcement learning"
    - "learning montezuma s revenge from a single demonstration"

Connected through hyperedge HE-3 (induced by source paper "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning powerful policies by using consistent dynamics model"
    - "mega reward achieving human level play without extrinsic rewards"
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"
    - "world models"

Connected through hyperedge HE-4 (induced by source paper "joint learning of unsupervised object based perception and control"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised learning of object keypoints for perception and control"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "multi object representation learning with iterative variational inference"
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational deep reinforcement learning"
    - "unsupervised video object segmentation for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "reasoning about physical interactions with object oriented prediction and planning"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "stochastic adversarial video prediction"
    - "unsupervised video object segmentation for deep reinforcement learning"
    - "deep object centric representations for generalizable robot learning"
    - "stochastic variational video prediction"
    - "neural expectation maximization"
    - "taking visual motion prediction to new heightfields"

Connected through hyperedge HE-6 (induced by source paper "sdrl interpretable and data efficient deep reinforcement learningleveraging symbolic planning"):
  with members:
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "robot representing and reasoning with knowledge from reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "explaining explanations an overview of interpretability of machine learning"
    - "eigenoption discovery through the deep successor representation"
    - "advantages and limitations of using successor features for transfer in reinforcement learning"

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)