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: "performance dynamics and success in online games"
  Abstract: Online data provide a way to monitor how users behave in social systems like social networks and online games, and understand which features turn an ordinary individual into a successful one. Here, we propose to study individual performance and success in Multiplayer Online Battle Arena (MOBA) games. Our purpose is to identify those behaviors and playing styles that are characteristic of players with high skill level and that distinguish them from other players. To this aim, we study Defense of the ancient 2 (Dota 2), a popular MOBA game. Our findings highlight three main aspects to be successful in the game: (i) players need to have a warm-up period to enhance their performance in the game; (ii) having a long in-game experience does not necessarily translate in achieving better skills; but rather, (iii) players that reach high skill levels differentiate from others because of their aggressive playing strategy, which implies to kill opponents more often than cooperating with teammates, and trying to give an early end to the match.

Connected through hyperedge HE-1 (induced by source paper "mining and forecasting career trajectories of music artists"):
  with members:
    - "discovering hidden structure in high dimensional human behavioral data via tensor factorization"
    - "birank towards ranking on bipartite graphs"
    - "style in the age of instagram predicting success within the fashion industry using social media"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "analysis of a heterogeneous social network of humans and cultural objects"
    - "deepwalk online learning of social representations"
    - "online popularity and topical interests through the lens of instagram"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks for optimal team composition"):
  with members:
    - "the art of drafting a team oriented hero recommendation system for multiplayer online battle arena games"
    - "graph embedding techniques applications and performance a survey"
    - "non negative tensor factorization for human behavioral pattern mining in online games"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "the influence of social ties on performance in team based online games"):
  with members:
    - "non negative tensor factorization for human behavioral pattern mining in online games"
    - "dynamics of content quality in collaborative knowledge production"
    - "evidence of online performance deterioration in user sessions on reddit"

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)