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 rate distortion"):
  Abstract: Consider a discrete memoryless multiple source with $m$ components of which $k \leq m$ possibly different sources are sampled at each time instant and jointly compressed in order to reconstruct all the $m$ sources under a given distortion criterion. A new notion of sampling rate distortion function is introduced, and is characterized first for the case of fixed-set sampling. Next, for independent random sampling performed without knowledge of the source outputs, it is shown that the sampling rate distortion function is the same regardless of whether or not the decoder is informed of the sequence of sampled sets. Furthermore, memoryless random sampling is considered with the sampler depending on the source outputs and with an informed decoder. It is shown that deterministic sampling, characterized by a conditional point-mass, is optimal and suffices to achieve the sampling rate distortion function. For memoryless random sampling with an uninformed decoder, an upper bound for the sampling rate distortion function is seen to possess a similar property of conditional point-mass optimality. It is shown by example that memoryless sampling with an informed decoder can outperform strictly any independent random sampler, and that memoryless sampling can do strictly better with an informed decoder than without.

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 "near optimal sensor placement for linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "greedy sampling of graph signals")
  Member "dass distributed adaptive sparse sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient sensor censoring for compressive distributed sparse signal recovery")
  Member "intersensor collaboration in distributed quantization networks":
    also connected through other hyperedges:
      (none)
  Member "lossy computing of correlated sources with fractional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reconstructing gaussian sources by spatial sampling")
  Member "sampling high dimensional bandlimited fields on low dimensional manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an algebraic geometric approach for linear regression without correspondences")
  Member "optimal phase transitions in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsourced multiuser sparse regression codes achieve the symmetric mac capacity")

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)