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: "talakat bullet hell generation through constrained map elites"
  Abstract: We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projectiles. Levels are represented using a domain-specific description language, and search in the space defined by this language is performed by a novel variant of the Map-Elites algorithm which incorporates a feasible- infeasible approach to constraint satisfaction. Simulation-based evaluation is used to gauge the fitness of levels, using an agent based on best-first search. The performance of the agent can be tuned according to the two dimensions of strategy and dexterity, making it possible to search for level configurations that require a specific combination of both. As far as we know, this paper describes the first generator for this game genre, and includes several algorithmic innovations.

Connected through hyperedge HE-1 (induced by source paper "mech elites illuminating the mechanic space of gvgai"):
  with members:
    - "general video game rule generation"
    - "procedural content generation through quality diversity"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "bootstrapping conditional gans for video game level generation"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "deep reinforcement learning for general video game ai"
    - "press space to fire automatic video game tutorial generation"

Connected through hyperedge HE-2 (induced by source paper "learning the designer s preferences to drive evolution"):
  with members:
    - "procedural content generation through quality diversity"
    - "pitako recommending game design elements in cicero"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "co creative level design via machine learning"
    - "procedural content generation via machine learning pcgml"
    - "machine teaching a new paradigm for building machine learning systems"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-3 (induced by source paper "mario level generation from mechanics using scene stitching"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "intentional computational level design"
    - "tile pattern kl divergence for analysing and evolving game levels"
    - "generating levels that teach mechanics"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-4 (induced by source paper "interactive constrained map elites analysis and evaluation of the expressiveness of the feature dimensions"):
  with members:
    - "procedural content generation through quality diversity"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "data agent"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-5 (induced by source paper "covariance matrix adaptation for the rapid illumination of behavior space"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "procedural content generation through quality diversity"
    - "alphastar an evolutionary computation perspective"
    - "evaluating map elites on constrained optimization problems"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"

Connected through hyperedge HE-6 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "quality diversity through surprise"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "adaptive music composition for games"
    - "deceptive games"

Connected through hyperedge HE-7 (induced by source paper "autonomous skill discovery with quality diversity and unsupervised descriptors"):
  with members:
    - "real world evolution adapts robot morphology and control to hardware limitations"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "hierarchical behavioral repertoires with unsupervised descriptors"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
    - "quality and diversity optimization a unifying modular framework"
    - "robust lstm autoencoders for face de occlusion in the wild"
    - "robots that can adapt like animals"

Connected through hyperedge HE-8 (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"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "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"

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)