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 "language in our time an empirical analysis of hashtags"):
  Abstract: Hashtags in online social networks have gained tremendous popularity during the past five years. The resulting large quantity of data has provided a new lens into modern society. Previously, researchers mainly rely on data collected from Twitter to study either a certain type of hashtags or a certain property of hashtags. In this paper, we perform the first large-scale empirical analysis of hashtags shared on Instagram, the major platform for hashtag-sharing. We study hashtags from three different dimensions including the temporal-spatial dimension, the semantic dimension, and the social dimension. Extensive experiments performed on three large-scale datasets with more than 7 million hashtags in total provide a series of interesting observations. First, we show that the temporal patterns of hashtags can be categorized into four different clusters, and people tend to share fewer hashtags at certain places and more hashtags at others. Second, we observe that a non-negligible proportion of hashtags exhibit large semantic displacement. We demonstrate hashtags that are more uniformly shared among users, as quantified by the proposed hashtag entropy, are less prone to semantic displacement. In the end, we propose a bipartite graph embedding model to summarize users' hashtag profiles, and rely on these profiles to perform friendship prediction. Evaluation results show that our approach achieves an effective prediction with AUC (area under the ROC curve) above 0.8 which demonstrates the strong social signals possessed in hashtags.

Includes members:
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "tagvisor a privacy advisor for sharing hashtags":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memory based graph networks")
  Member "you are your metadata identification and obfuscation of social media users using metadata information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks")
  Member "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign":
    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 "ml leaks model and data independent membership inference attacks and defenses on machine learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "twitter for sparking a movement reddit for sharing the moment metoo through the lens of social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "firsthand opiates abuse on social media monitoring geospatial patterns of interest through a digital cohort")
  Member "is saki delicious the food perception gap on instagram and its relation to health":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction")

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)