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: "lossy computing of correlated sources with fractional sampling"
  Abstract: This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. The rate-distortion function is characterized for memory-less sources, and then specialized to Gaussian and binary sources for selected functions and with quadratic and Hamming distortion metrics, respectively. The optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source statistics, including the correlation, and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy. Finally, the analysis is extended to the multi-hop set-up with jointly Gaussian sources, where each encoder can observe only one of the sources.

Connected through hyperedge HE-1 (induced by source paper "reconstructing gaussian sources by spatial sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "near optimal sensor placement for linear inverse problems"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "distributed source coding in dense sensor networks"

Connected through hyperedge HE-2 (induced by source paper "fundamental distortion limits of analog to digital compression"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "indirect rate distortion function of a binary i i d source"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "optimal phase transitions in compressed sensing"
    - "high resolution distributed sampling of bandlimited fields with low precision sensors"

Connected through hyperedge HE-3 (induced by source paper "sampling rate distortion"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "near optimal sensor placement for linear inverse problems"
    - "dass distributed adaptive sparse sensing"
    - "intersensor collaboration in distributed quantization networks"
    - "sampling high dimensional bandlimited fields on low dimensional manifolds"
    - "optimal phase transitions in compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "fundamentals of large sensor networks connectivity capacity clocks and computation"

Connected through hyperedge HE-4 (induced by source paper "compression with actions"):
  with members:
    - "estimation with a helper who knows the interference"
    - "lossy compression of discrete sources via the viterbi algorithm"
    - "information embedding meets distributed control"

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)