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 "waiting but not aging age of information and utility optimization under the pull model"):
  Abstract: The Age-of-Information (AoI) has recently been proposed as an important metric for investigating the timeliness performance in information-update systems. In this paper, we introduce a new Pull model and study the AoI optimization problem under replication schemes. Interestingly, we find that under this new Pull model, replication schemes capture a novel tradeoff between different levels of information freshness and different response times across the servers, which can be exploited to minimize the expected AoI at the user's side. Specifically, assuming Poisson updating process for the servers and exponentially distributed response time, we derive a closed-form formula for computing the expected AoI and obtain the optimal number of responses to wait for to minimize the expected AoI. Then, we extend our analysis to the setting where the user aims to maximize the AoI-based utility, which represents the user's satisfaction level with respect to freshness of the received information. Furthermore, we consider a more realistic scenario where the user has no knowledge of the system. In this case, we reformulate the utility maximization problem as a stochastic Multi-Armed Bandit problem with side observations and leverage the unique structure of the problem to design learning algorithms with improved performance guarantees. Finally, we conduct extensive simulations to elucidate our theoretical results and compare the performance of different algorithms. Our findings reveal that under the Pull model, waiting for more than one response can significantly reduce the AoI and improve the AoI-based utility in most scenarios.

Includes members:
  Member "combinatorial sleeping bandits with fairness constraints":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey")
  Member "the age of information in networks moments distributions and sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "minimizing content staleness in dynamo style replicated storage systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "age of information for updates with distortion constant and age dependent distortion constraints")
  Member "optimizing information freshness in wireless networks under general interference constraints":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "optimizing age of information in wireless networks with perfect channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "status updates through multicast networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "who should google scholar update more often")
  Member "the age of information in multihop networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "the power of waiting for more than one response in minimizing the age of information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards the tradeoff between service performance and information freshness")

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.NI(Networking and Internet Architecture)