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: "mapping hearthstone deck spaces through map elites with sliding boundaries"
  Abstract: Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evolutionary robotics problems such as locomotion and maze navigation, but have yet to see widespread application. We argue that these algorithms are perfectly suited to the rich domain of video games, which contains many relevant problems with a multitude of successful strategies and often also multiple dimensions along which solutions can vary. #R##N#This paper introduces a novel modification of the MAP-Elites algorithm called MAP-Elites with Sliding Boundaries (MESB) and applies it to the design and rebalancing of Hearthstone, a popular collectible card game chosen for its number of multidimensional behavior features relevant to particular styles of play. To avoid overpopulating cells with conflated behaviors, MESB slides the boundaries of cells based on the distribution of evolved individuals. Experiments in this paper demonstrate the performance of MESB in Hearthstone. Results suggest MESB finds diverse ways of playing the game well along the selected behavioral dimensions. Further analysis of the evolved strategies reveals common patterns that recur across behavioral dimensions and explores how MESB can help rebalance the game.

Connected through hyperedge HE-1 (induced by source paper "automating representation discovery with map elites"):
  with members:
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "deep interactive evolution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "evolving deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "the many ai challenges of hearthstone"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"

Connected through hyperedge HE-3 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "quality diversity through surprise"
    - "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"
    - "talakat bullet hell generation through constrained map elites"
    - "deceptive games"

Connected through hyperedge HE-4 (induced by source paper "the many ai challenges of hearthstone"):
  with members:
    - "evolving the hearthstone meta"
    - "co creative level design via machine learning"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "atdelfi automatically designing legible full instructions for games"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "latent predictor networks for code generation"
    - "robots that can adapt like animals"

Connected through hyperedge HE-5 (induced by source paper "evolving the hearthstone meta"):
  with members:
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "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.NE(Neural and Evolutionary Computing)