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: "compressive sensing over networks"
  Abstract: In this paper, we demonstrate some applications of compressive sensing over networks. We make a connection between compressive sensing and traditional information theoretic techniques in source coding and channel coding. Our results provide an explicit trade-off between the rate and the decoding complexity. The key difference of compressive sensing and traditional information theoretic approaches is at their decoding side. Although optimal decoders to recover the original signal, compressed by source coding have high complexity, the compressive sensing decoder is a linear or convex optimization. First, we investigate applications of compressive sensing on distributed compression of correlated sources. Here, by using compressive sensing, we propose a compression scheme for a family of correlated sources with a modularized decoder, providing a trade-off between the compression rate and the decoding complexity. We call this scheme Sparse Distributed Compression. We use this compression scheme for a general multicast network with correlated sources. Here, we first decode some of the sources by a network decoding technique and then, we use a compressive sensing decoder to obtain the whole sources. Then, we investigate applications of compressive sensing on channel coding. We propose a coding scheme that combines compressive sensing and random channel coding for a high-SNR point-to-point Gaussian channel. We call this scheme Sparse Channel Coding. We propose a modularized decoder providing a trade-off between the capacity loss and the decoding complexity. At the receiver side, first, we use a compressive sensing decoder on a noisy signal to obtain a noisy estimate of the original signal and then, we apply a traditional channel coding decoder to find the original signal.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

Connected through hyperedge HE-2 (induced by source paper "how to distribute computation in networks"):
  with members:
    - "on the derivative imbalance and ambiguity of functions"
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "lagrange coded computing optimal design for resiliency security and privacy"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "on heterogeneous coded distributed computing"
    - "a fundamental tradeoff between computation and communication in distributed computing"
    - "comments on cut set bounds on network function computation"
    - "computing linear functions by linear coding over networks"

Connected through hyperedge HE-3 (induced by source paper "distributed quantization for sparse time sequences"):
  with members:
    - "serial quantization for representing sparse signals"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "distributed compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "distributed quantization for compressed sensing"):
  with members:
    - "performance bounds for vector quantized compressive sensing"
    - "distributed functional scalar quantization simplified"
    - "analysis by synthesis quantization for compressed sensing measurements"
    - "message passing de quantization with applications to compressed sensing"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "optimal quantization for compressive sensing under message passing reconstruction"
    - "universal rate efficient scalar quantization"
    - "distributed compressive sensing"

Connected through hyperedge HE-5 (induced by source paper "machine learning in wireless sensor networks algorithms strategies and applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "in network outlier detection in wireless sensor networks"
    - "exact soft confidence weighted learning"
    - "structured compressed sensing from theory to applications"
    - "distributed learning in wireless sensor networks"

Connected through hyperedge HE-6 (induced by source paper "non adaptive distributed compression in networks"):
  with members:
    - "bayesian quantized network coding via belief propagation"
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for sparse messages"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-7 (induced by source paper "quantized network coding for correlated sources"):
  with members:
    - "non adaptive distributed compression in networks"
    - "bayesian quantized network coding via belief propagation"
    - "one step quantized network coding for near sparse gaussian messages"
    - "quantized network coding for sparse messages"
    - "restricted isometry property in quantized network coding of sparse messages"
    - "sparse recovery with graph constraints fundamental limits and measurement construction"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "compressive sensing over graphs"

Connected through hyperedge HE-8 (induced by source paper "restricted isometry property in quantized network coding of sparse messages"):
  with members:
    - "quantized network coding for sparse messages"
    - "a power efficient sensing communication scheme joint source channel network coding by using compressive sensing"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "compressed sensing how sharp is the restricted isometry property"
    - "phase transitions for greedy sparse approximation algorithms"
    - "network information flow with correlated sources"

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)