human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "remote estimation over a packet drop channel with markovian state"
  Abstract: We investigate a remote estimation problem in which a transmitter observes a Markov source and chooses the power level to transmit it over a time-varying packet-drop channel. The channel is modeled as a channel with Markovian state where the packet drop probability depends on the channel state and the transmit power. A receiver observes the channel output and the channel state and estimates the source realization. The receiver also feeds back the channel state and an acknowledgment for successful reception to the transmitter. We consider two models for the source---finite state Markov chains and first-order autoregressive processes. For the first model, using ideas from team theory, we establish the structure of optimal transmission and estimation strategies and identify a dynamic program to determine optimal strategies with that structure. For the second model, we assume that the noise process has unimodal and symmetric distribution. Using ideas from majorization theory, we show that the optimal transmission strategy is symmetric and monotonic and the optimal estimation strategy is like Kalman filter. Consequently, when there are a finite number of power levels, the optimal transmission strategy may be described using thresholds that depend on the channel state. Finally, we propose a simulation based approach (Renewal Monte Carlo) to compute the optimal thresholds and optimal performance and elucidate the algorithm with an example.

Connected through hyperedge HE-1 (induced by source paper "who should google scholar update more often"):
  with members:
    - "optimal selective encoding for timely updates"
    - "trading off computation with transmission in status update systems"
    - "average peak age of information minimization in uav assisted iot networks"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information aware cache updating with file and age dependent update durations"
    - "reducing age of information for computation intensive messages via packet replacement"
    - "timely distributed computation with stragglers"
    - "age of information in multicast networks with multiple update streams"

Connected through hyperedge HE-2 (induced by source paper "sampling for remote estimation through queues age of information and beyond"):
  with members:
    - "age optimal updates of multiple information flows"
    - "tutorial on uav a blue sky view on wireless communication"
    - "information aging through queues a mutual information perspective"
    - "a dynamic jamming game for real time status updates"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "quantifying link stability in ad hoc wireless networks subject to ornstein uhlenbeck mobility"
    - "optimizing information freshness in wireless networks under general interference constraints"

Connected through hyperedge HE-3 (induced by source paper "sampling of the wiener process for remote estimation over a channel with random delay"):
  with members:
    - "the dispersion of the gauss markov source"
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "age optimal information updates in multihop networks"
    - "remote estimation of the wiener process over a channel with random delay"
    - "the distortion rate function of sampled wiener processes"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "the age of information real time status updating by multiple sources"

Question: Please tell me which class the center node 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)