human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "diverse behavior is what game ai needs generating varied human like playing styles using evolutionary multi objective deep reinforcement learning"):
  Abstract: Designing artificial intelligence for games (Game AI) has been long recognized as a notoriously challenging task in the game industry, as it mainly relies on manual design, requiring plenty of domain knowledge. More frustratingly, even spending a lot of effort, a satisfying Game AI is still hard to achieve by manual design due to the almost infinite search space. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game designing, significantly relaxing human competitive intelligent support. However, existing DRL algorithms mostly focus on training a Game AI to win the game rather than the way it wins (style). To bridge the gap, we introduce EMO-DRL, an end-to-end game design framework, leveraging evolutionary algorithm, DRL and multi-objective optimization (MOO) to perform intelligent and automatic game design. Firstly, EMO-DRL proposes style-oriented learning to bypass manual reward shaping in DRL and directly learns a Game AI with an expected style in an end-to-end fashion. On this basis, the prioritized multi-objective optimization is introduced to achieve more diverse, nature and human-like Game AI. Large-scale evaluations on an Atari game and a commercial massively multiplayer online game are conducted. The results demonstrate that EMO-DRL, compared to existing algorithms, achieve better game designs in an intelligent and automatic way.

Includes members:
  Member "a generalized framework for population based training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents")

Question: Please tell me which class the source paper 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.LG(Machine Learning)