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 "on the minimax capacity loss under sub nyquist universal sampling"):
  Abstract: This paper investigates the information rate loss in analog channels when the sampler is designed to operate independent of the instantaneous channel occupancy. Specifically, a multiband linear time-invariant Gaussian channel under universal sub-Nyquist sampling is considered. The entire channel bandwidth is divided into $n$ subbands of equal bandwidth. At each time only $k$ constant-gain subbands are active, where the instantaneous subband occupancy is not known at the receiver and the sampler. We study the information loss through a capacity loss metric, that is, the capacity gap caused by the lack of instantaneous subband occupancy information. We characterize the minimax capacity loss for the entire sub-Nyquist rate regime, provided that the number $n$ of subbands and the SNR are both large. The minimax limits depend almost solely on the band sparsity factor and the undersampling factor, modulo some residual terms that vanish as $n$ and SNR grow. Our results highlight the power of randomized sampling methods (i.e. the samplers that consist of random periodic modulation and low-pass filters), which are able to approach the minimax capacity loss with exponentially high probability.

Includes members:
  Member "sampling rate distortion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "correlated bandits or how to minimize mean squared error online")
  Member "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "minimax capacity loss under sub nyquist universal sampling")
  Member "channel capacity under sub nyquist nonuniform sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the information bottleneck problems models connections applications and information theoretic views")
  Member "shannon meets nyquist capacity of sampled gaussian channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the capacity of memoryless channels with sampled cyclostationary gaussian noise")
  Member "living at the edge a large deviations approach to the outage mimo capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gallager bound for mimo channels large n asymptotics")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image guided depth sampling and reconstruction")
  Member "increased capacity per unit cost by oversampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bandlimited communication with one bit quantization and oversampling transceiver design and performance evaluation")
  Member "condition numbers of gaussian random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")

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)