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: "pilot symbol assisted communications with noncausal and causal wiener filters"
  Abstract: In this paper, pilot-assisted transmission over time-selective flat fading channels is studied. It is assumed that noncausal and causal Wiener filters are employed at the receiver to perform channel estimation with the aid of training symbols sent periodically by the transmitter. For both filters, the variances of estimate errors are obtained from the Doppler power spectrum of the channel. Subsequently, achievable rate expressions are provided. The training period, and data and training power allocations are jointly optimized by maximizing the achievable rate expressions. Numerical results are obtained by modeling the fading as a Gauss-Markov process. The achievable rates of causal and noncausal filtering approaches are compared. For the particular ranges of parameters considered in the paper, the performance loss incurred by using a causal filter as opposed to a noncausal filter is shown to be small. The impact of aliasing that occurs in the undersampled version of the channel Doppler spectrum due to fast fading is analyzed. Finally, energy-per-bit requirements are investigated in the presence of noncausal and causal Wiener filters.

Connected through hyperedge HE-1 (induced by source paper "ergodic capacity analysis in cognitive radio systems under channel uncertainty"):
  with members:
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "wideband spectrum sensing in cognitive radio networks"
    - "training optimization for gauss markov rayleigh fading channels"

Connected through hyperedge HE-2 (induced by source paper "on the capacity and energy efficiency of training based transmissions over fading channels"):
  with members:
    - "training optimization for gauss markov rayleigh fading channels"
    - "capacity of sparse multipath channels in the ultra wideband regime"
    - "the noncoherent rician fading channel part i structure of the capacity achieving input"
    - "noncoherent rician fading channel part ii spectral efficiency in the low power regime"
    - "channel uncertainty in ultra wideband communication systems"
    - "capacity per unit energy of fading channels with a peak constraint"

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)