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 "how often should i access my online social networks"):
  Abstract: Users of online social networks are faced with a conundrum of trying to be always informed without having enough time or attention budget to do so. The retention of users on online social networks has important implications, encompassing economic, psychological and infrastructure aspects. In this paper, we pose the following question: what is the optimal rate at which users should access a social network? To answer this question, we propose an analytical model to determine the value of an access (VoA) to the social network. In the simple setting considered in this paper, VoA is defined as the chance of a user accessing the network and obtaining new content. Clearly, VoA depends on the rate at which sources generate content and on the filtering imposed by the social network. Then, we pose an optimization problem wherein the utility of users grows with respect to VoA but is penalized by costs incurred to access the network. Using the proposed framework, we provide insights on the optimal access rate. Our results are parameterized using Facebook data, indicating the predictive power of the approach.

Includes members:
  Member "age of information vs value of information scheduling for cellular networked control systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "revealing much while saying less predictive wireless for status update")
  Member "performance analysis of online social platforms":
    also connected through other hyperedges:
      (none)
  Member "minimizing the age of information in wireless networks with stochastic arrivals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "age of information for multicast transmission with fixed and random deadlines in iot systems")
  Member "biases in the facebook news feed a case study on the italian elections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairness in online social network timelines measurements models and mechanism design")
  Member "fairness in online social network timelines measurements models and mechanism design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantifying the impact of user attention on fair group representation in ranked lists")
  Member "age optimal sampling and transmission scheduling in multi source systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely status updating through intermittent sensing and transmission")
  Member "optimal status update for age of information minimization with an energy harvesting source":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "who should google scholar update more often")
  Member "characterizing information diets of social media users":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vocabulary based method for quantifying controversy in social media")

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)