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 "when to preempt age of information minimization under link capacity constraint"):
  Abstract: In this paper, we consider a scenario where a source continuously monitors an object and sends time-stamped status updates to a destination through a rate-limited link. We assume updates arrive randomly at the source according to a Bernoulli process. Due to the link capacity constraint, it takes multiple time slots for the source to complete the transmission of an update. Therefore, when a new update arrives at the source during the transmission of another update, the source needs to decide whether to skip the new arrival or to switch to it, in order to minimize the expected average age of information (AoI) at the destination. We start with the setting where all updates are of the same size, and prove that within a broadly defined class of online policies, the optimal policy should be a renewal policy, and has a sequential switching property. We then show that the optimal decision of the source in any time slot has threshold structures, and only depends on the age of the update being transmitted and the AoI at the destination. We then consider the setting where updates are of different sizes, and show that the optimal Markovian policy also has a multiple-threshold structure. For each of the settings, we explicitly identify the thresholds by formulating the problem as a Markov Decision Process (MDP), and solve it through value iteration. Special structural properties of the corresponding optimal policy are utilized to reduce the computational complexity of the value iteration algorithm.

Includes members:
  Member "age optimal updates of multiple information flows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "scheduling policies for minimizing age of information in broadcast wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who should google scholar update more often")
  Member "optimal status updating for an energy harvesting sensor with a noisy channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "timely status updating through intermittent sensing and transmission")
  Member "to skip or to switch minimizing age of information under link capacity constraint":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "controlling packet drops to improve freshness of 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 "optimizing information freshness in wireless networks under general interference constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "age minimal online policies for energy harvesting sensors with random battery recharges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "who should google scholar update more often")
  Member "multicast with prioritized delivery how fresh is your data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "partial updates losing information for 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.IT(Information Theory)