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 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.

Connected through hyperedge HE-1 (induced by source paper "sparse regression codes"):
  with members:
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "sparcs for unsourced random access"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"

Connected through hyperedge HE-2 (induced by source paper "learning to compress and search visual data in large scale systems"):
  with members:
    - "rendition reclaiming what a black box takes away"
    - "polysemous codes"
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "billion scale similarity search with gpus"
    - "hashnet deep learning to hash by continuation"
    - "memnet a persistent memory network for image restoration"
    - "full resolution image compression with recurrent neural networks"
    - "end to end optimized image compression"

Connected through hyperedge HE-3 (induced by source paper "the rate distortion function and excess distortion exponent of sparse regression codes with optimal encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "chasing the k colorability threshold"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-4 (induced by source paper "polar lattices for lossy compression"):
  with members:
    - "some goodness properties of lda lattices"
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "construction of capacity achieving lattice codes polar lattices"
    - "on the construction of polar codes"
    - "wyner ziv coding based on multidimensional nested lattices"
    - "how to construct polar codes"
    - "polar codes are optimal for lossy source coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-5 (induced by source paper "lossy compression via sparse linear regression computationally efficient encoding and decoding"):
  with members:
    - "sparse regression codes for multi terminal source and channel coding"
    - "fast sparse superposition codes have exponentially small error probability for r l c"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "the dispersion of lossy source coding"
    - "least squares superposition codes of moderate dictionary size reliable at rates up to capacity"
    - "toward fast reliable communication at rates near capacity with gaussian noise"
    - "polar codes are optimal for lossy source coding"

Connected through hyperedge HE-6 (induced by source paper "rateless lossy compression via the extremes"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "the gaussian rate distortion function of sparse regression codes with optimal encoding"
    - "network compression worst case analysis"
    - "fixed length lossy compression in the finite blocklength regime"
    - "incremental refinement using a gaussian test channel"
    - "the dispersion of joint source channel coding"
    - "polar codes are optimal for lossy source coding"
    - "rateless coding for gaussian channels"

Connected through hyperedge HE-7 (induced by source paper "the gaussian rate distortion function of sparse regression codes with optimal encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "chasing the k colorability threshold"
    - "fast sparse superposition codes have exponentially small error probability for r l c"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-8 (induced by source paper "the rate distortion function and error exponent of sparse regression codes with optimal encoding"):
  with members:
    - "lossy compression via sparse linear regression computationally efficient encoding and decoding"
    - "chasing the k colorability threshold"
    - "fast sparse superposition codes have exponentially small error probability for r l c"

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)