human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sampling stationary signals subject to bitrate constraints"):
  Abstract: A theory of minimizing distortion in reconstructing a stationary signal under a constraint on the number of bits per sample is developed. We first analyze the optimal sampling frequency required in order to achieve the optimal distortion-rate tradeoff for a stationary bandlimited signal. To this end, we consider a combined sampling and source coding problem in which an analog Gaussian source is reconstructed from its encoded sub-Nyquist samples. We show that for processes whose energy is not uniformly distributed over the spectral band, each point on the distortion-rate curve of the process corresponds to a sampling frequency smaller than the Nyquist rate. This characterization can be seen as an extension of the classical sampling theorem for bandlimited random processes in the sense that it describes the minimal amount of excess distortion in the reconstruction due to lossy compression of the samples, and provides the minimal sampling frequency $f_{DR}$ required in order to achieve that distortion. We compare the fundamental limits of combined source coding and sampling to the performance in pulse code modulation (PCM), where each sample is quantized by a scalar quantizer using a fixed number of bits.

Includes members:
  Member "distortion rate function of sub nyquist sampled gaussian sources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "analog to digital compression a new paradigm for converting signals to bits")
  Member "channel capacity under sub nyquist nonuniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "achieving the gaussian rate distortion function by prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the rate distortion function of asynchronously sampled memoryless cyclostationary gaussian processes")

Question: Please tell me which class the source paper 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)