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: "adaptive shooting for bots in first person shooter games using reinforcement learning"
  Abstract: In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as non player characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, "sixth sense" knowledge of human players' whereabouts and deterministic, scripted behaviors are some of the causes of this. We propose, however, that one of the biggest indicators of non humanlike behavior in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and "locking on" to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable non player characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.

Connected through hyperedge HE-1 (induced by source paper "automated video game testing using synthetic and human like agents"):
  with members:
    - "computer games are serious business and so is their quality particularities of software testing in game development from the perspective of practitioners"

Connected through hyperedge HE-2 (induced by source paper "skilled experience catalogue a skill balancing mechanism for non player characters using reinforcement learning"):
  with members:
    - "learning to shoot in first person shooter games by stabilizing actions and clustering rewards for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "vizdoom a doom based ai research platform for visual reinforcement learning"):
  with members:
    - "exploratory gradient boosting for reinforcement learning in complex domains"

Connected through hyperedge HE-4 (induced by source paper "multi agent inverse reinforcement learning for zero sum games"):
  with members:
    - "move evaluation in go using deep convolutional neural networks"
    - "computational rationalization the inverse equilibrium problem"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

Connected through hyperedge HE-5 (induced by source paper "multi agent inverse reinforcement learning for two person zero sum games"):
  with members:
    - "move evaluation in go using deep convolutional neural networks"
    - "inverse reinforcement learning with gaussian process"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

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)