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 "optimal sampling and scheduling for timely status updates in multi source networks"):
  Abstract: In this paper, we consider a joint sampling and transmission scheduling problem for optimizing data freshness in a multi-source system, where the sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. The freshness of the received packets is measured by a non-decreasing penalty function of the age of information, where the same age-penalty function is used for all sources. The decision maker consists of two components: A scheduler that chooses the update order of the sources, and a sampler that determines when a source should generate a new packet in its turn. Our goal is to find the optimal scheduler-sampler pairs that minimize the total-average peak age-penalty (Ta-PAP) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler, in which a new packet is generated once the channel becomes idle, are jointly optimal for minimizing the Ta-PAP. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration (RVI) algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman's equation. In the special case of a linear age-penalty function, this threshold-type sampler reduces to a simple water-filling sampler. The MAF scheduler and threshold-type sampler are very close to the optimum for minimizing the Ta-AP in our numerical evaluations.

Includes members:
  Member "optimizing information freshness using low power status updates via sleep wake scheduling":
    also connected through other hyperedges:
      (none)
  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 "distributed scheduling algorithms for optimizing information freshness in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  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 "optimizing age of information in wireless networks with perfect channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "scheduling policies for age minimization in wireless networks with unknown channel state":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")

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)