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 "lossy compression via sparse linear regression performance under minimum distance encoding"):
  Abstract: We study a new class of codes for lossy compression with the squared-error distortion criterion, designed using the statistical framework of high-dimensional linear regression. Codewords are linear combinations of subsets of columns of a design matrix. Called a sparse superposition or sparse regression codebook, this structure is motivated by an analogous construction proposed recently by Barron and Joseph for communication over an Additive White Gaussian Noise channel. For independent identically distributed (i.i.d) Gaussian sources and minimum-distance encoding, we show that such a code can attain the Shannon rate-distortion function with the optimal error exponent, for all distortions below a specified value. It is also shown that sparse regression codes are robust in the following sense: a codebook designed to compress an i.i.d Gaussian source of variance    $\sigma^{2}$    with (squared-error) distortion    $D$    can compress any ergodic source of variance less than    $\sigma^{2}$    to within distortion    $D$   . Thus, the sparse regression ensemble retains many of the good covering properties of the i.i.d random Gaussian ensemble, while having a compact representation in terms of a matrix whose size is a low-order polynomial in the block-length.

Includes members:
  Member "lossy compression via sparse linear regression computationally efficient encoding and decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression codes")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finite sample analysis of image registration")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse regression codes")
  Member "least squares superposition codes of moderate dictionary size reliable at rates up to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse regression codes")
  Member "toward fast reliable communication at rates near capacity with gaussian noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")
  Member "polar codes are optimal for lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "necessary and sufficient conditions for sparsity pattern recovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "harmless interpolation of noisy data in regression")
  Member "rate distortion via markov chain monte carlo":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse regression codes")

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)