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: "scaling laws in geo located twitter data"
  Abstract: We observe and report on a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power laws, with exponents greater than one, that are consistent with each other and across a range of spatial scales. This implies that population density can accurately predict Twitter activity. Furthermore this trend can be used to identify `anomalous' areas that deviate from the trend. Analysis of geo-tagged and place-tagged tweets show that geo-tagged tweets are different with respect to user type and content. Our findings have implications for the spatial analysis of Twitter data and for understanding demographic biases in the Twitter user base.

Connected through hyperedge HE-1 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election"):
  with members:
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "using word embeddings in twitter election classification"
    - "distributed representations of sentences and documents"
    - "deep learning of representations looking forward"
    - "distributed representations of words and phrases and their compositionality"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-2 (induced by source paper "the human geography of twitter"):
  with members:
    - "depicting urban boundaries from a mobility network of spatial interactions a case study of great britain with geo located twitter data"
    - "dialectometric analysis of language variation in twitter"
    - "demographic dialectal variation in social media a case study of african american english"
    - "delineating geographical regions with networks of human interactions in an extensive set of countries"

Connected through hyperedge HE-3 (induced by source paper "english verb regularization in books and tweets"):
  with members:
    - "mapping the americanization of english in space and time"
    - "is language evolution grinding to a halt the scaling of lexical turbulence in english fiction suggests it is not"

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)