human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "information is not a virus and other consequences of human cognitive limits"):
  with members:
    - "the majority illusion in social networks"
    - "the simple rules of social contagion"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "network weirdness exploring the origins of network paradoxes"
    - "friendship paradox redux your friends are more interesting than you"
    - "stochastic models predict user behavior in social media"
    - "information diffusion and external influence in networks"
    - "social dynamics of digg"

Connected through hyperedge HE-2 (induced by source paper "emotions demographics and sociability in twitter interactions"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "cultures in community question answering"
    - "sentiment cascades in the 15m movement"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "comparing and combining sentiment analysis methods"
    - "the role of social networks in information diffusion"
    - "positive words carry less information than negative words"

Connected through hyperedge HE-3 (induced by source paper "social ties and emotions evidence from social media"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "cultures in community question answering"
    - "sentiment cascades in the 15m movement"
    - "big questions for social media big data representativeness validity and other methodological pitfalls"
    - "comparing and combining sentiment analysis methods"
    - "positive words carry less information than negative words"

Question: Please tell me which class the center node 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)