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: "the distortion rate function of cyclostationary gaussian processes"
  Abstract: A general expression for the distortion rate function (DRF) of a cyclostationary Gaussian process in terms of its spectral properties is derived. This DRF is given by water-filling over the eigenvalues of a spectral density matrix associated with the polyphase components of the process. We use this expression to derive, in a closed form, the DRF of processes obtained by two important modulation techniques. We first show that the DRF of a bandlimited narrow-band Gaussian stationary process modulated by a deterministic sine-wave equals the DRF of the baseband process. We then show that the DRF of a Gaussian stationary process modulated by a train of pulses is given by reverse waterfilling over a PSD which is an aliased version of the energy of the pulse and the base process. Moreover, this last result is used to derive the indirect distortion-rate function of a stationary Gaussian process given its sub-Nyquist samples.

Connected through hyperedge HE-1 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes"):
  with members:
    - "the capacity of memoryless channels with sampled cyclostationary gaussian noise"
    - "on the capacity of narrowband plc channels"
    - "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
    - "on the optimal compressions in the compress and forward relay schemes"
    - "the generalized asymptotic equipartition property necessary and sufficient conditions"
    - "achieving the gaussian rate distortion function by prediction"

Connected through hyperedge HE-2 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits"):
  with members:
    - "the distortion rate function of sampled wiener processes"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "speech recognition with deep recurrent neural networks"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-3 (induced by source paper "on the sample complexity of graphical model selection for non stationary processes"):
  with members:
    - "network lasso clustering and optimization in large graphs"
    - "noncoherent capacity of underspread fading channels"
    - "information theoretic limits of selecting binary graphical models in high dimensions"
    - "consistency of the group lasso and multiple kernel learning"

Connected through hyperedge HE-4 (induced by source paper "the distortion rate function of sampled wiener processes"):
  with members:
    - "analog to digital compression a new paradigm for converting signals to bits"
    - "remote estimation of the wiener process over a channel with random delay"
    - "compress and estimate in multiterminal source coding"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"

Connected through hyperedge HE-5 (induced by source paper "distortion rate function of sub nyquist sampled gaussian sources"):
  with members:
    - "channel capacity under sub nyquist nonuniform sampling"
    - "minimax capacity loss under sub nyquist universal sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-6 (induced by source paper "waterfilling theorems for linear time varying channels and related nonstationary sources"):
  with members:
    - "waterfilling theorems in the time frequency plane for the heat channel and a related source"
    - "eigenvalue estimates and mutual information for the linear time varying channel"
    - "noncoherent capacity of underspread fading channels"

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)