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 "learning from the news predicting entity popularity on twitter"):
  Abstract: In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learn- ing approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high or low in the following hours. We run several experiments on six different entities in a dataset of over 150M tweets and 5M news and obtained F1 scores over 0.70. Error analysis indicates that news perform better on predicting entity popularity on Twitter when they are the primary information source of the event, in opposition to events such as live TV broadcasts, political debates or football matches.

Includes members:
  Member "timemachine entity centric search and visualization of news archives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "early fusion strategy for entity relationship retrieval")
  Member "breaking the news first impressions matter on online news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentiment dynamics in social media news channels")
  Member "popmine tracking political opinion on the web":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transportation in social media an automatic classifier for travel related tweets")
  Member "characterizing the life cycle of online news stories using social media reactions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scilens evaluating the quality of scientific news articles using social media and scientific literature indicators")
  Member "the pulse of news in social media forecasting popularity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling the popularity of twitter hashtags with master equations")
  Member "dynamical classes of collective attention in twitter":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "language in our time an empirical analysis of hashtags")

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)