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 estimation with limited measurements and noisy communication"
  Abstract: This paper considers a sequential estimation and sensor scheduling problem with one sensor and one estimator. The sensor makes sequential observations about the state of an underlying memoryless stochastic process, and makes a decision as to whether or not to send this measurement to the estimator. The sensor and the estimator have the common objective of minimizing expected distortion in the estimation of the state of the process, over a finite time horizon, with the constraint that the sensor can transmit its observation only a limited number of times. As opposed to the prior work where communication between the sensor and the estimator was assumed to be perfect (noiseless), in this work an additive noise channel with fixed power constraint is considered; hence, the sensor has to encode its message before transmission. For some specific source and channel noise densities, we obtain the optimal encoding and estimation policies in conjunction with the optimal transmission schedule. The impact of the presence of a noisy channel is analyzed numerically based on dynamic programming. This analysis yields some rather surprising results such as a phase-transition phenomenon in the number of used transmission opportunities, which was not encountered in the noiseless communication setting.

Connected through hyperedge HE-1 (induced by source paper "optimal causal rate constrained sampling of the wiener process"):
  with members:
    - "remote estimation of the wiener process over a channel with random delay"
    - "event triggered stabilization of linear systems under bounded bit rates"
    - "infinite horizon optimal transmission power control for remote state estimation over fading channels"
    - "the distortion rate function of sampled wiener processes"
    - "rate cost tradeoffs in control"
    - "optimal estimation via nonanticipative rate distortion function and applications to time varying gauss markov processes"
    - "event triggered control under time varying rates and channel blackouts"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"

Connected through hyperedge HE-2 (induced by source paper "can we achieve fresh information with selfish users in mobile crowd learning"):
  with members:
    - "timely updates over an erasure channel"
    - "status updates over unreliable multiaccess channels"
    - "remote estimation of the wiener process over a channel with random delay"
    - "update or wait how to keep your data fresh"

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"
    - "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-4 (induced by source paper "remote estimation of the wiener process over a channel with random delay"):
  with members:
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "age optimal information updates in multihop networks"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "on remote estimation with multiple communication channels"
    - "distortion transmission trade off in real time transmission of markov sources"
    - "optimal strategies for communication and remote estimation with an energy harvesting sensor"
    - "paging and registration in cellular networks jointly optimal policies and an iterative algorithm"

Connected through hyperedge HE-5 (induced by source paper "on remote estimation with multiple communication channels"):
  with members:
    - "optimal sensor scheduling and remote estimation over an additive noise channel"
    - "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)