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 "debatenight the role and influence of socialbots on twitter during the 1st u s presidential debate"):
  Abstract: Serious concerns have been raised about the role of 'socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets during the 1st U.S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1.5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness. First, we define a measure of user influence based on the user's active contributions to information diffusions, i.e. their tweets and retweets. Given that Twitter does not expose the retweet structure - it associates all retweets with the original tweet - we model the latent diffusion structure using only tweet time and user features, and we implement a scalable novel approach to estimate influence over all possible unfoldings. Next, we use partisan hashtag analysis to quantify user political polarization and engagement. Finally, we use the BotOrNot API to measure user botness (the likelihood of being a bot). We build a two-dimensional "polarization map" that allows for a nuanced analysis of the interplay between botness, partisanship and influence. We find that not only social bots are more active on Twitter - starting more retweet cascades and retweeting more -- but they are 2.5 times more influential than humans, and more politically engaged. Moreover, pro-Republican bots are both more influential and more politically engaged than their pro-Democrat counterparts. However we caution against blanket statements that software designed to appear human dominates political debates. We find that many highly influential Twitter users are in fact pro-Democrat and that most pro-Republican users are mid-influential and likely to be human (low botness).

Includes members:
  Member "expecting to be hip hawkes intensity processes for social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "online human bot interactions detection estimation and characterization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "botornot a system to evaluate social bots":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning")
  Member "feature driven and point process approaches for popularity prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")
  Member "modeling events with cascades of poisson processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a multi channel neural graphical event model with negative evidence")
  Member "uncovering the temporal dynamics of diffusion networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "novelty and collective attention":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling the popularity of twitter hashtags with master equations")

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)