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: "america tweets china a fine grained analysis of the state and individual characteristics regarding attitudes towards china"
  Abstract: The U.S.-China relationship is arguably the most important bilateral relationship in the 21st century. Typically it is measured through opinion polls, for example, by Gallup and Pew Institute. In this paper, we propose a new method to measure U.S.-China relations using data from Twitter, one of the most popular social networks. Compared with traditional opinion polls, our method has two distinctive advantages. First, our sample size is significantly larger. National opinion polls have at most a few thousand samples. Our data set has 724,146 samples. The large size of our data set enables us to perform state level analysis, which so far even large opinion polls have left unexplored. Second, our method can control for fixed state and date effects. We first demonstrate the existence of inter-state and inter-day variances and then control for these variances in our regression analysis. Empirically, our study is able to replicate the stylized results from opinion polls as well as generate new insights. At the state level, we find New York, Michigan, Indiana and Arizona are the top four most China-friendly states. Wyoming, South Dakota, Kansas and Nevada are most homogeneous. At the individual level, we find attitudes towards China improve as an individual's Twitter experience grows longer and more intense. We also find individuals of Chinese ethnicity are statistically more China-friendly.

Connected through hyperedge HE-1 (induced by source paper "tracking illicit drug dealing and abuse on instagram using multimodal analysis"):
  with members:
    - "finding street gang members on twitter"
    - "webly supervised learning of convolutional networks"
    - "pinterest board recommendation for twitter users"
    - "social media image analysis for public health"
    - "monitoring potential drug interactions and reactions via network analysis of instagram user timelines"
    - "going deeper with convolutions"

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)