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: "player behavior and optimal team composition for online multiplayer games"
  Abstract: We consider clustering player behavior and learning the optimal team composition for multiplayer online games. The goal is to determine a set of descriptive play style groupings and learn a predictor for win/loss outcomes. The predictor takes in as input the play styles of the participants in each team; i.e., the various team compositions in a game. Our framework uses unsupervised learning to find behavior clusters, which are, in turn, used with classification algorithms to learn the outcome predictor. For our numerical experiments, we consider League of Legends, a popular team-based role-playing game developed by Riot Games. We observe the learned clusters to not only corroborate well with game knowledge, but also provide insights surprising to expert players. We also demonstrate that game outcomes can be predicted with fairly high accuracy given team composition-based features.

Connected through hyperedge HE-1 (induced by source paper "scalable psychological momentum forecasting in esports"):
  with members:
    - "hierarchical reinforcement learning for multi agent moba game"
    - "on the convergence of adam and beyond"
    - "deep neural networks for optimal team composition"
    - "player skill decomposition in multiplayer online battle arenas"

Connected through hyperedge HE-2 (induced by source paper "moba slice a time slice based evaluation framework of relative advantage between teams in moba games"):
  with members:
    - "searching for activation functions"
    - "on the development of intelligent agents for moba games"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "the proficiency congruency dilemma virtual team design and performance in multiplayer online games"):
  with members:
    - "exploring cyberbullying and other toxic behavior in team competition online games"
    - "analyzing organizational routines in online knowledge collaborations a case for sequence analysis in cscw"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"
    - "friends ftw friendship and competition in halo reach"
    - "friends ftw friendship and competition in halo reach"

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.SI(Social and Information Networks)