human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "online reactions to the 2017 unite the right rally in charlottesville measuring polarization in twitter networks using media followership"):
  Abstract: We study the Twitter conversation following the August 2017 `Unite the Right' rally in Charlottesville, Virginia, using tools from network analysis and data science. We use media followership on Twitter and principal component analysis (PCA) to compute a `Left'/`Right' media score on a one-dimensional axis to characterize nodes. We then use these scores, in concert with retweet relationships, to examine the structure of a retweet network of approximately 300,000 accounts that communicated with the #Charlottesville hashtag. The retweet network is sharply polarized, with an assortativity coefficient of 0.8 with respect to the sign of the media PCA score. Community detection using two approaches, a Louvain method and InfoMap, yields largely homogeneous communities in terms of Left/Right node composition. When comparing tweet content, we find that tweets about `Trump' were widespread in both the Left and Right, though the accompanying language was unsurprisingly different. Nodes with large degrees in communities on the Left include accounts that are associated with disparate areas, including activism, business, arts and entertainment, media, and politics. Support of Donald Trump was a common thread among the Right communities, connecting communities with accounts that reference white-supremacist hate symbols, communities with influential personalities in the alt-right, and the largest Right community (which includes the Twitter account FoxNews).

Includes members:
  Member "social network analysis for social neuroscientists":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "migration networks applications of network analysis to large scale human mobility")
  Member "its all in a name detecting and labeling bots by their name":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable and generalizable social bot detection through data selection")
  Member "centrality in modular networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "movienet a movie multilayer network model using visual and textual semantic cues")
  Member "a quantitative approach to understanding online antisemitism":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic search of memes on twitter")
  Member "who let the trolls out towards understanding state sponsored trolls":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "on the origins of memes by means of fringe web communities":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the pushshift reddit dataset")
  Member "a long term analysis of polarization on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ideotrace a framework for ideology tracing with a case study on the 2016 u s presidential election")
  Member "botornot a system to evaluate social bots":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")

Question: Please tell me which class the source paper 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)