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: "what is the nature of tencent weibo detect the unique features of tencent users"
  Abstract: Tencent Weibo has become the world largest Micro-Bloggings website within less than three years, which contains 370 million users, who contribute 30-60 million micro-blogs (we call weibo) for each day. Different with Twitters, Tencent provides users more permissions to share different kinds of information, such as more relaxed limitation of text length, pictures, videos and etc, which could help users to build their personalized micro media center. The main purpose of this paper is to take deep insight into Tencent Weibo and study its unique features, which are different from other famous Micro-Bloggings weibsite. We have collected the entire Tencent Weibo from 10th, Oct, 2011 to 1st, Dec, 2012 and obtained 37.1 million user profiles, 41.2 billion users' behaviors. We have made analysis for Tencent users' different behaviors from both macro and micro level, and make comparison with other social networks such as Twitter, Delicious and et al. We find that from macro level, Tencent Weibo is more inclined to be a platform of social media, rather than a communication network; compared with Twitter, Tencent Weibo has more complex network and more active users. From micro level, Tencent users are more interested in creating and sharing original messages; compared with Twitter, Tencent users are more likely to discuss on a certain number of old topics, they have a lower reciprocity rate, a stronger connection with smaller number of their followers; besides, there exists correlation for users' different behaviors from statistical level; at last, we find that users' behaviors have a positive correlation with the number of their followers in a limited extent, and the relation will invalid when it exceeds that extent. To the best of our knowledge, this work is the first quantitative study on the entire Tencentsphere and information diffusion on it.

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)