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 "modelling online comment threads from their start"):
  Abstract: The social Web is a widely used platform for online discussion. Across social media, users can start discussions by posting a topical image, url, or message. Upon seeing this initial post, other users may add their own comments to the post, or to another user's comment. The resulting online discourse produces a comment thread, which constitutes an enormous portion of modern online communication. Comment threads are often viewed as trees: nodes represent the post and its comments, while directed edges represent reply-to relationships. The goal of the present work is to predict the size and shape of these comment threads. Existing models do this by observing the first several comments and then fitting a predictive model. However, most comment threads are relatively small, and waiting for data to materialize runs counter to the goal of the prediction task. We therefore introduce the Comment Thread Prediction Model (CTPM) that accurately predicts the size and shape of a comment thread using only the text of the initial post, allowing for the prediction of new posts without observable comments. We find that the CTPM significantly outperforms existing models and competitive baselines on thousands of Reddit discussions from nine varied subreddits, particularly for new posts.

Includes members:
  Member "modelling structure and predicting dynamics of discussion threads in online boards":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the pushshift reddit dataset")
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "guessthekarma a game to assess social rating systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quantifying voter biases in online platforms an instrumental variable approach")
  Member "rating effects on social news posts and comments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "characterizing behavioral trends in a community driven discussion platform")
  Member "deep reinforcement learning with a combinatorial action space for predicting and tracking popular discussion threads":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arming the public with artificial intelligence to counter social bots")
  Member "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning")
  Member "tideh time dependent hawkes process for predicting retweet dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep graph matching consensus")

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)