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 "infinite horizon optimal transmission power control for remote state estimation over fading channels"):
  Abstract: Jointly optimal transmission power control and remote estimation over an infinite horizon is studied. A sensor observes a dynamic process and sends its observations to a remote estimator over a wireless fading channel characterized by a time-homogeneous Markov chain. The successful transmission probability depends on both the channel gains and the transmission power used by the sensor. The transmission power control rule and the remote estimator should be jointly designed, aiming to minimize an infinite-horizon cost consisting of the power usage and the remote estimation error. A first question one may ask is: Does this joint optimization problem have a solution? We formulate the joint optimization problem as an average cost belief-state Markov decision process and answer the question by proving that there exists an optimal deterministic and stationary policy. We then show that when the monitored dynamic process is scalar, the optimal remote estimates depend only on the most recently received sensor observation, and the optimal transmission power is symmetric and monotonically increasing with respect to the innovation error.

Includes members:
  Member "multi sensor scheduling for state estimation with event based stochastic triggers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "event based state estimation an emulation based approach")
  Member "on the existence of optimal policies for a class of static and sequential dynamic teams":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non signaling approximations of stochastic team problems")
  Member "distortion transmission trade off in real time transmission of markov sources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "remote estimation of the wiener process over a channel with random delay")
  Member "stochastic event triggered sensor scheduling for remote state estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "event triggered diffusion kalman filters")
  Member "design of state based schedulers for a network of control loops":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for scheduling in large scale networked control systems")
  Member "optimal pruning for multi step sensor scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sensor scheduling in variance based event triggered estimation with packet drops")
  Member "discretized approximations for pomdp with average cost":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  Member "optimal strategies for communication and remote estimation with an energy harvesting sensor":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal causal rate constrained sampling for a class of continuous markov processes")

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)