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: "a bayesian ensemble regression framework on the angry birds game"
  Abstract: An ensemble inference mechanism is proposed on the Angry Birds domain. It is based on an efficient tree structure for encoding and representing game screenshots, where it exploits its enhanced modeling capability. This has the advantage to establish an informative feature space and modify the task of game playing to a regression analysis problem. To this direction, we assume that each type of object material and bird pair has its own Bayesian linear regression model. In this way, a multi-model regression framework is designed that simultaneously calculates the conditional expectations of several objects and makes a target decision through an ensemble of regression models. Learning procedure is performed according to an online estimation strategy for the model parameters. We provide comparative experimental results on several game levels that empirically illustrate the efficiency of the proposed methodology.

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"
    - "bejeweled candy crush and other match three games are np hard"
    - "towards a theory of mind inspired generic decision making framework"
    - "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"
    - "towards a theory of mind inspired generic decision making framework"

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"
    - "towards a theory of mind inspired generic decision making framework"

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)