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 "sampling for data freshness optimization non linear age functions"):
  Abstract: In this paper, we study how to take samples at a data source for improving the freshness of received data samples at a remote receiver. We use non-linear functions of the age of information to measure data freshness, and provide a survey of non-linear age functions and their applications. The sampler design problem is studied to optimize these data freshness metrics, even when there is a sampling rate constraint. This sampling problem is formulated as a constrained Markov decision process (MDP) with a possibly uncountable state space. We present a complete characterization of the optimal solution to this MDP: The optimal sampling policy is a deterministic or randomized threshold policy, where the threshold and the randomization probabilities are characterized based on the optimal objective value of the MDP and the sampling rate constraint. The optimal sampling policy can be computed by bisection search, and the curse of dimensionality is circumvented. These age optimality results hold for (i) general data freshness metrics represented by monotonic functions of the age of information, (ii) general service time distributions of the queueing server, (iii) both continuoustime and discrete-time sampling problems, and (iv) sampling problems both with and without the sampling rate constraint. Numerical results suggest that the optimal sampling policies can be much better than zero-wait sampling and the classic uniform sampling.

Includes members:
  Member "sampling for remote estimation through queues age of information and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "urgency of information for context aware timely status updates in remote control systems")
  Member "performance characterization using aoi in a single loop networked control system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probability analysis of age of information in multi hop networks")
  Member "age optimal updates of multiple information flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "the age of information in networks moments distributions and sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "timely updates by multiple sources the m m 1 queue revisited")
  Member "information aging through queues a mutual information perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "who should google scholar update more often")
  Member "a dynamic jamming game for real time status updates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a non cooperative multiple access game for timely updates")
  Member "joint status sampling and updating for minimizing age of information in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "age of information with gilbert elliot servers and samplers")
  Member "optimizing information freshness in wireless networks under general interference constraints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")

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.SY(Systems and Control)