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 "user effort and network structure mediate access to information in networks"):
  Abstract: Individuals' access to information in a social network depends on its distributed and where in the network individuals position themselves. However, individuals have limited capacity to manage their social connections and process information. In this work, we study how this limited capacity and network structure interact to affect the diversity of information social media users receive. Previous studies of the role of networks in information access were limited in their ability to measure the diversity of information. We address this problem by learning the topics of interest to social media users by observing messages they share online with their followers. We present a probabilistic model that incorporates human cognitive constraints in a generative model of information sharing. We then use the topics learned by the model to measure the diversity of information users receive from their social media contacts. We confirm that users in structurally diverse network positions, which bridge otherwise disconnected regions of the follower graph, are exposed to more diverse information. In addition, we identify user effort as an important variable that mediates access to diverse information in social media. Users who invest more effort into their activity on the site not only place themselves in more structurally diverse positions within the network than the less engaged users, but they also receive more diverse information when located in similar network positions. These findings indicate that the relationship between network structure and access to information in networks is more nuanced than previously thought.

Includes members:
  Member "vip incorporating human cognitive biases in a probabilistic model of retweeting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "information pollution by social bots")
  Member "stochastic models predict user behavior in social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "continuous time user modeling in the presence of badges a probabilistic approach")
  Member "structural and cognitive bottlenecks to information access in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "measuring emotional contagion in social media")
  Member "la ctr a limited attention collaborative topic regression for social media":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tribeflow mining predicting user trajectories")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "collaborative topic regression with social matrix factorization for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "semi supervised learning meets factorization learning to recommend with chain graph model")
  Member "social dynamics of digg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep collaborative embedding for information cascade 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)