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: "towards a theory of mind inspired generic decision making framework"
  Abstract: Simulation is widely used to make model-based predictions, but few approaches have attempted this technique in dynamic physical environments of medium to high complexity or in general contexts. After an introduction to the cognitive science concepts from which this work is inspired and the current development in the use of simulation as a decision-making technique, we propose a generic framework based on theory of mind, which allows an agent to reason and perform actions using multiple simulations of automatically created or externally inputted models of the perceived environment. A description of a partial implementation is given, which aims to solve a popular game within the IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison with the competition benchmark. Finally, future developments regarding the framework are discussed.

Connected through hyperedge HE-1 (induced by source paper "the computational complexity of angry birds"):
  with members:
    - "the computational complexity of portal and other 3d video games"
    - "pspace completeness of bloxorz and of games with 2 buttons"
    - "braid is undecidable"
    - "a bayesian ensemble regression framework on the angry birds game"
    - "bejeweled candy crush and other match three games are np hard"
    - "candy crush is np hard"
    - "classic nintendo games are computationally hard"
    - "lemmings is pspace complete"

Connected through hyperedge HE-2 (induced by source paper "agent based adaptive level generation for dynamic difficulty adjustment in angry birds"):
  with members:
    - "the 2017 aibirds competition"
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "a bayesian ensemble regression framework on the angry birds game"

Connected through hyperedge HE-3 (induced by source paper "the 2017 aibirds competition"):
  with members:
    - "deep learning for video game playing"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "ai researchers video games are your friends"
    - "a bayesian ensemble regression framework on the angry birds game"

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)