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: "decentralized estimation over orthogonal multiple access fading channels in wireless sensor networks optimal and suboptimal estimators"
  Abstract: We study optimal and suboptimal decentralized estimators in wireless sensor networks over orthogonal multiple-access fading channels in this paper. Considering multiple-bit quantization for digital transmission, we develop maximum likelihood estimators (MLEs) with both known and unknown channel state information (CSI). When training symbols are available, we derive a MLE that is a special case of the MLE with unknown CSI. It implicitly uses the training symbols to estimate CSI and exploits channel estimation in an optimal way and performs the best in realistic scenarios where CSI needs to be estimated and transmission energy is constrained. To reduce the computational complexity of the MLE with unknown CSI, we propose a suboptimal estimator. These optimal and suboptimal estimators exploit both signal- and data-level redundant information to combat the observation noise and the communication errors. Simulation results show that the proposed estimators are superior to the existing approaches, and the suboptimal estimator performs closely to the optimal MLE.

Connected through hyperedge HE-1 (induced by source paper "the effect of correlated observations on the performance of distributed estimation"):
  with members:
    - "controlled collaboration for linear coherent estimation in wireless sensor networks"
    - "on scaling laws of diversity schemes in decentralized estimation"
    - "estimation diversity and energy efficiency in distributed sensing"

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)