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: "power control and coding formulation for state estimation with wireless sensors"
  Abstract: Technological advances made wireless sensors cheap and reliable enough to be brought into industrial use. A major challenge arises from the fact that wireless channels introduce random packet dropouts. Power control and coding are key enabling technologies in wireless communications to ensure efficient communication. In this paper, we examine the role of power control and coding for Kalman filtering over wireless correlated channels. Two estimation architectures are considered; initially, the sensors send their measurements directly to a single gateway (GW). Next, wireless relay nodes provide additional links. The GW decides on the coding scheme and the transmitter power levels of the wireless nodes. The decision process is carried out online and adapts to varying channel conditions to improve the tradeoff between state estimation accuracy and energy expenditure. In combination with predictive power control, we investigate the use of multiple-description coding (MDC), zero-error coding (ZEC), and network coding and provide sufficient conditions for the expectation of the estimation error covariance matrix to be bounded. Numerical results suggest that the proposed method may lead to energy savings of around 50%, when compared with an alternative scheme, wherein transmission power levels and bit-rates are governed by simple logic. In particular, ZEC is preferable at time instances with high channel gains, whereas MDC is superior for time instances with low gains. When channels between the sensors and the GW are in deep fades, network coding improves estimation accuracy significantly without sacrificing energy efficiency.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for wireless sensor scheduling in cyber physical systems"):
  with members:
    - "deepcas a deep reinforcement learning algorithm for control aware scheduling"
    - "sensor scheduling in variance based event triggered estimation with packet drops"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

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)