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: "decision theoretic planning structural assumptions and computational leverage"
  Abstract: Planning under uncertainty is a central problem in the study of automated sequential decision making, and has been addressed by researchers in many different fields, including AI planning, decision analysis, operations research, control theory and economics. While the assumptions and perspectives adopted in these areas often differ in substantial ways, many planning problems of interest to researchers in these fields can be modeled as Markov decision processes (MDPs) and analyzed using the techniques of decision theory.#R##N##R##N#This paper presents an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI. It also describes structural properties of MDPs that, when exhibited by particular classes of problems, can be exploited in the construction of optimal or approximately optimal policies or plans. Planning problems commonly possess structure in the reward and value functions used to describe performance criteria, in the functions used to describe state transitions and observations, and in the relationships among features used to describe states, actions, rewards, and observations.#R##N##R##N#Specialized representations, and algorithms employing these representations, can achieve computational leverage by exploiting these various forms of structure. Certain AI techniques-- in particular those based on the use of structured, intensional representations--can be viewed in this way. This paper surveys several types of representations for both classical and decision-theoretic planning problems, and planning algorithms that exploit these representations in a number of different ways to ease the computational burden of constructing policies or plans. It focuses primarily on abstraction, aggregation and decomposition techniques based on AI-style representations.

Connected through hyperedge HE-1 (induced by source paper "optimal by design model driven synthesis of adaptation strategies for autonomous systems"):
  with members:
    - "perpetual assurances for self adaptive systems"
    - "a storm is coming a modern probabilistic model checker"
    - "on the complexity of solving markov decision problems"

Connected through hyperedge HE-2 (induced by source paper "a model based decision theoretic perspective on automated cyber response"):
  with members:
    - "despot online pomdp planning with regularization"
    - "efficient planning under uncertainty with macro actions"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-3 (induced by source paper "reachability and differential based heuristics for solving markov decision processes"):
  with members:
    - "structured reachability analysis for markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "robby is not a robber anymore on the use of institutions for learning normative behavior"):
  with members:
    - "norms institutions and robots"
    - "learning dexterous in hand manipulation"
    - "unity a general platform for intelligent agents"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "proximal policy optimization algorithms"
    - "concrete problems in ai safety"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "scalable nonlinear planning with deep neural network learned transition models"):
  with members:
    - "compact and efficient encodings for planning in factored state and action spaces with learned binarized neural network transition models"
    - "scalable planning with tensorflow for hybrid nonlinear domains"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional networks"
    - "a hybrid lp rpg heuristic for modelling numeric resource flows in planning"
    - "playing atari with deep reinforcement learning"
    - "do deep nets really need to be deep"
    - "solving factored mdps with hybrid state and action variables"

Connected through hyperedge HE-6 (induced by source paper "a sufficient statistic for influence in structured multiagent environments"):
  with members:
    - "near optimal behavior via approximate state abstraction"
    - "tractable inference for complex stochastic processes"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "a bilinear programming approach for multiagent planning"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-7 (induced by source paper "learning compact models for planning with exogenous processes"):
  with members:
    - "discovering and removing exogenous state variables and rewards for reinforcement learning"
    - "variance reduction for reinforcement learning in input driven environments"
    - "mine mutual information neural estimation"
    - "contingency aware exploration in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "state continuity approximation of markov decision processes via finite element analysis for autonomous system planning"):
  with members:
    - "data driven learning and planning for environmental sampling"
    - "optimal and learning control for autonomous robots"
    - "a solution to time varying markov decision processes"

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)