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: "distortion transmission trade off in real time transmission of markov sources"
  Abstract: The problem of optimal real-time transmission of a Markov source under constraints on the expected number of transmissions is considered, both for the discounted and long term average cases. This setup is motivated by applications where transmission is sporadic and the cost of switching on the radio and transmitting is significantly more important than the size of the transmitted data packet. For this model, we characterize the distortion-transmission function, i.e., the minimum expected distortion that can be achieved when the expected number of transmissions is less than or equal to a particular value. In particular, we show that the distortion-transmission function is a piecewise linear, convex, and decreasing function. We also give an explicit characterization of each vertex of the piecewise linear function. #R##N#To prove the results, the optimization problem is cast as a decentralized constrained stochastic control problem. We first consider the Lagrange relaxation of the constrained problem and identify the structure of optimal transmission and estimation strategies. In particular, we show that the optimal transmission is of a threshold type. Using these structural results, we obtain dynamic programs for the Lagrange relaxations. We identify the performance of an arbitrary threshold-type transmission strategy and use the idea of calibration from multi-armed bandits to determine the optimal transmission strategy for the Lagrange relaxation. Finally, we show that the optimal strategy for the constrained setup is a randomized strategy that randomizes between two deterministic strategies that differ only at one state. By evaluating the performance of these strategies, we determine the shape of the distortion-transmission function. These results are illustrated using an example of transmitting a birth-death Markov source.

Connected through hyperedge HE-1 (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"
    - "optimal estimation with limited measurements and noisy communication"
    - "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-2 (induced by source paper "infinite horizon optimal transmission power control for remote state estimation over fading channels"):
  with members:
    - "multi sensor scheduling for state estimation with event based stochastic triggers"
    - "on the existence of optimal policies for a class of static and sequential dynamic teams"
    - "stochastic event triggered sensor scheduling for remote state estimation"
    - "design of state based schedulers for a network of control loops"
    - "optimal pruning for multi step sensor scheduling"
    - "discretized approximations for pomdp with average cost"
    - "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"

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.IT(Information Theory)