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 "reconstructing gaussian sources by spatial sampling"):
  Abstract: Consider a Gaussian memoryless multiple source with $m$ components with joint probability distribution known only to lie in a given class of distributions. A subset of $k \leq m$ components are sampled and compressed with the objective of reconstructing all the $m$ components within a specified level of distortion under a mean-squared error criterion. In Bayesian and nonBayesian settings, the notion of universal sampling rate distortion function for Gaussian sources is introduced to capture the optimal tradeoffs among sampling, compression rate and distortion level. Single-letter characterizations are provided for the universal sampling rate distortion function. Our achievability proofs highlight the following structural property: it is optimal to compress and reconstruct first the sampled components of the GMMS alone, and then form estimates for the unsampled components based on the former.

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 "sampling rate distortion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "correlated bandits or how to minimize mean squared error online")
  Member "near optimal sensor placement for linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "greedy sampling of graph signals")
  Member "lossy computing of correlated sources with fractional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fundamental distortion limits of analog to digital compression")
  Member "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")
  Member "distributed source coding in dense sensor networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling rate distortion")

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)