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 model learning and monte carlo tree search in minecraft"
  Abstract: Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model predicts the next frame and the rewards one step ahead given the last four frames of the agent's first-person-view image and the current action. Then a Monte Carlo tree search algorithm uses this model to plan the best sequence of actions for the agent to perform. On the proposed task in Minecraft, our model-based approach reaches the performance comparable to the Deep Q-Network's, but learns faster and, thus, is more training sample efficient.

Connected through hyperedge HE-1 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning for atari"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "when to use parametric models in reinforcement learning"
    - "benchmarking model based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "learning real world robot policies by dreaming"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model based regularization for deep reinforcement learning with transcoder networks"

Connected through hyperedge HE-3 (induced by source paper "craftassist a framework for dialogue enabled interactive agents"):
  with members:
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "habitat a platform for embodied ai research"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "talk the walk navigating new york city through grounded dialogue"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (induced by source paper "craftassist instruction parsing semantic parsing for a minecraft assistant"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "naturalizing a programming language via interactive learning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "opennmt open source toolkit for neural machine translation"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "hierarchical and interpretable skill acquisition in multi task reinforcement learning"
    - "learning language games through interaction"

Connected through hyperedge HE-5 (induced by source paper "deep recurrent q learning vs deep q learning on a simple partially observable markov decision process with minecraft"):
  with members:
    - "obstacle tower a generalization challenge in vision control and planning"
    - "efficient exploration through bayesian deep q networks"
    - "teacher student curriculum learning"
    - "deep learning for video game playing"
    - "proximal policy optimization algorithms"
    - "playing fps games with deep reinforcement learning"
    - "control of memory active perception and action in minecraft"
    - "trust region policy optimization"

Connected through hyperedge HE-6 (induced by source paper "model based stabilisation of deep reinforcement learning"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "learning and querying fast generative models for reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "value prediction network"
    - "recurrent environment simulators"
    - "curiosity driven exploration by self supervised prediction"
    - "a deep learning approach for joint video frame and reward prediction in atari games"
    - "reinforcement learning with unsupervised auxiliary tasks"

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)