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: "optimal sensor scheduling and remote estimation over an additive noise channel"
  Abstract: We consider a sensor scheduling and remote estimation problem with one sensor and one estimator. At each time step, the sensor makes an observation on the state of a source, and then decides whether to transmit its observation to the estimator or not. The sensor is charged a cost for each transmission. The remote estimator generates a real-time estimate on the state of the source based on the messages received from the sensor. The estimator is charged for estimation error. As compared with previous works from the literature, we further assume that there is an additive communication channel noise. As a consequence, the sensor needs to encode the message before transmitting it to the estimator. For some specific distributions of the underlying random variables, we obtain the optimal solution to the problem of minimizing the expected value of the sum of communication cost and estimation cost over the time horizon.

Connected through hyperedge HE-1 (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"
    - "remote estimation over a packet drop channel with markovian state"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "on remote estimation with multiple communication channels"):
  with members:
    - "optimal estimation with limited measurements and noisy communication"
    - "on zero delay source channel coding"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-3 (induced by source paper "optimal estimation with limited measurements and noisy communication"):
  with members:
    - "on zero delay source channel coding"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

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)