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: "q deckrec a fast deck recommendation system for collectible card games"
  Abstract: Deck building is a crucial component in playing Collectible Card Games (CCGs). The goal of deck building is to choose a fixed-sized subset of cards from a large card pool, so that they work well together in-game against specific opponents. Existing methods either lack flexibility to adapt to different opponents or require large computational resources, still making them unsuitable for any real-time or large-scale application. We propose a new deck recommendation system, named Q-DeckRec, which learns a deck search policy during a training phase and uses it to solve deck building problem instances. Our experimental results demonstrate Q-DeckRec requires less computational resources to build winning-effective decks after a training phase compared to several baseline methods.

Connected through hyperedge HE-1 (induced by source paper "pcgrl procedural content generation via reinforcement learning"):
  with members:
    - "using fractal neural networks to play simcity 1 and conway s game of life at variable scales"
    - "tree search vs optimization approaches for map generation"
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "deep learning for video game playing"
    - "procedural content generation via machine learning pcgml"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "evolutionary approach to collectible card game arena deckbuilding using active genes"):
  with members:
    - "the many ai challenges of hearthstone"
    - "evolving the hearthstone meta"
    - "demonstrating the feasibility of automatic game balancing"

Connected through hyperedge HE-3 (induced by source paper "mapping hearthstone deck spaces through map elites with sliding boundaries"):
  with members:
    - "monte carlo tree search for simulation based strategy analysis"
    - "talakat bullet hell generation through constrained map elites"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "demonstrating the feasibility of automatic game balancing"
    - "robots that can adapt like animals"
    - "evolving a behavioral repertoire for a walking robot"

Connected through hyperedge HE-4 (induced by source paper "evolving the hearthstone meta"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "exploring gameplay with ai agents"
    - "demonstrating the feasibility of automatic game balancing"
    - "illuminating search spaces by mapping elites"

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)