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: "oversampling increases the pre log of noncoherent rayleigh fading channels"
  Abstract: We analyze the capacity of a continuous-time, time- selective, Rayleigh block-fading channel in the high signal-to-noise ratio regime. The fading process is assumed stationary within each block and to change independently from block to block; further- more, its realizations are not known a priori to the transmitter and the receiver (noncoherent setting). A common approach to analyzing the capacity of this channel is to assume that the receiver performs matched filtering followed by sampling at symbol rate (symbol matched filtering). This yields a discrete-time channel in which each transmitted symbol corresponds to one output sample. Liang & Veeravalli (2004) showed that the capacity of this discrete- time channel grows logarithmically with the signal-to noise ratio (SNR), with a capacity pre-log equal to 1 − Q/N . Here, N is the number of symbols transmitted within one fading block, and Q is the rank of the covariance matrix of the discrete-time channel gains within each fading block. In this paper, we show that sym- bol matched filtering is not a capacity-achieving strategy for the underlying continuous-time channel. Specifically, we analyze the capacity pre-log of the discrete-time channel obtained by oversam- pling the continuous-time channel output, i.e., by sampling it faster than at symbol rate. We prove that by oversampling by a factor two one gets a capacity pre-log that is at least as large as 1 − 1/N . Since the capacity pre-log corresponding to symbol-rate sampling is 1−Q/N , our result implies indeed that symbol matched filtering is not capacity achieving at high SNR.

Connected through hyperedge HE-1 (induced by source paper "models and information rates for wiener phase noise channels"):
  with members:
    - "lower bound on the capacity of continuous time wiener phase noise channels"
    - "upper bound on the capacity of discrete time wiener phase noise channels"
    - "on continuous time white phase noise channels"
    - "phase modulation for discrete time wiener phase noise channels with oversampling at high snr"
    - "capacity bounds for mimo microwave backhaul links affected by phase noise"
    - "multi sample receivers increase information rates for wiener phase noise channels"
    - "on wiener phase noise channels at high signal to noise ratio"
    - "calculation of mutual information for partially coherent gaussian channels with applications to fiber optics"

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)