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: "learning partially observable deterministic action models"
  Abstract: We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model (the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains.#R##N##R##N#Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AI-planning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventure-game playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis.

Connected through hyperedge HE-1 (induced by source paper "strips action discovery"):
  with members:
    - "learning strips action models with classical planning"
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "pddlgym gym environments from pddl problems"):
  with members:
    - "glib exploration via goal literal babbling for lifted operator learning"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "moleculenet a benchmark for molecular machine learning"
    - "planning with noisy probabilistic relational rules"
    - "learning symbolic models of stochastic domains"

Connected through hyperedge HE-3 (induced by source paper "learning strips action models with classical planning"):
  with members:
    - "hierarchical finite state controllers for generalized planning"
    - "efficient safe and probably approximately complete learning of action models"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-4 (induced by source paper "learning generalized models by interrogating black box autonomous agents"):
  with members:
    - "learning strips action models with classical planning"
    - "efficient safe and probably approximately complete learning of action models"

Connected through hyperedge HE-5 (induced by source paper "the text based adventure ai competition"):
  with members:
    - "deep reinforcement learning that matters"
    - "curiosity driven exploration by self supervised prediction"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "text based adventures of the golovin ai agent"
    - "what can you do with a rock affordance extraction via word embeddings"
    - "bag of tricks for efficient text classification"
    - "skip thought vectors"
    - "ai researchers video games are your friends"

Connected through hyperedge HE-6 (induced by source paper "compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models"):
  with members:
    - "action schema networks generalised policies with deep learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "natural language processing almost from scratch"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-7 (induced by source paper "interactive learning of state representation through natural language instruction and explanation"):
  with members:
    - "learning strips operators from noisy and incomplete observations"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-8 (induced by source paper "deterministic pomdps revisited"):
  with members:
    - "heuristic search value iteration for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "the computational complexity of probabilistic 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)