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: "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.

Connected through hyperedge HE-1 (induced by source paper "correlated bandits or how to minimize mean squared error online"):
  with members:
    - "reconstructing gaussian sources by spatial sampling"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-2 (induced by source paper "sniper efficient multi scale training"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "mixed precision training"
    - "an analysis of scale invariance in object detection snip"
    - "ssh single stage headless face detector"

Connected through hyperedge HE-3 (induced by source paper "reconstructing gaussian sources by spatial sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "near optimal sensor placement for linear inverse problems"
    - "lossy computing of correlated sources with fractional sampling"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "distributed source coding in dense sensor networks"

Connected through hyperedge HE-4 (induced by source paper "fundamental distortion limits of analog to digital compression"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "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"
    - "lossy computing of correlated sources with fractional sampling"
    - "optimal phase transitions in compressed sensing"
    - "high resolution distributed sampling of bandlimited fields with low precision sensors"

Connected through hyperedge HE-5 (induced by source paper "the distortion rate function of sampled wiener processes"):
  with members:
    - "analog to digital compression a new paradigm for converting signals to bits"
    - "remote estimation of the wiener process over a channel with random delay"
    - "compress and estimate in multiterminal source coding"
    - "fundamental distortion limits of analog to digital compression"
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "the distortion rate function of cyclostationary gaussian processes"

Connected through hyperedge HE-6 (induced by source paper "sampling and distortion tradeoffs for indirect source retrieval"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "compressive sampling of ensembles of correlated signals"
    - "sampling and distortion tradeoffs for bandlimited periodic signals"
    - "time stampless adaptive nonuniform sampling for stochastic signals"

Connected through hyperedge HE-7 (induced by source paper "sampling and distortion tradeoffs for bandlimited periodic signals"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "sample distortion for compressed imaging"
    - "time stampless adaptive nonuniform sampling for stochastic signals"
    - "optimal sampling points in reproducing kernel hilbert spaces"
    - "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "optimal phase transitions in compressed sensing"

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)