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 computationally efficient encoding and decoding"
  Abstract: We propose computationally efficient encoders and decoders for lossy compression using a sparse regression code. The codebook is defined by a design matrix and codewords are structured linear combinations of columns of this matrix. The proposed encoding algorithm sequentially chooses columns of the design matrix to successively approximate the source sequence. It is shown to achieve the optimal distortion-rate function for independent identically distributed (i.i.d) Gaussian sources under the squared-error distortion criterion. For a given rate, the parameters of the design matrix can be varied to tradeoff distortion performance with encoding complexity. An example of such a tradeoff as a function of the block length    $n$    is the following. With computational resource (space or time) per source sample of    $O((n/\log n)^{2})$   , for a fixed distortion-level above the Gaussian distortion-rate function, the probability of excess distortion decays exponentially in    $n$   . The sparse regression code is robust in the following sense: for any ergodic source, the proposed encoder achieves the optimal distortion-rate function of an i.i.d Gaussian source with the same variance. Simulations show that the encoder has good empirical performance, especially at low and moderate rates.

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 subfield codes of hyperoval and conic codes"):
  with members:
    - "the subfield codes of ovoid codes"
    - "binary linear codes from vectorial boolean functions and their weight distribution"
    - "linear codes from some 2 designs"
    - "five constructions of permutation polynomials over gf q 2"

Connected through hyperedge HE-4 (induced by source paper "minimal linear codes over finite fields"):
  with members:
    - "a construction of binary linear codes from boolean functions"
    - "linear codes from some 2 designs"

Connected through hyperedge HE-5 (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 performance under minimum distance encoding"
    - "chasing the k colorability threshold"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"

Connected through hyperedge HE-6 (induced by source paper "universality of logarithmic loss in lossy compression"):
  with members:
    - "justification of logarithmic loss via the benefit of side information"
    - "rateless lossy compression via the extremes"
    - "multiterminal source coding under logarithmic loss"
    - "multiterminal source coding with an entropy based distortion measure"

Connected through hyperedge HE-7 (induced by source paper "a class of linear codes with a few weights"):
  with members:
    - "a construction of binary linear codes from boolean functions"
    - "linear codes from a generic construction"
    - "linear codes from some 2 designs"
    - "linear codes with two or three weights from quadratic bent functions"
    - "a class of three weight and five weight linear codes"
    - "a class of linear codes with three weights"
    - "linear codes with two or three weights from weakly regular bent functions"
    - "a class of two weight and three weight codes and their applications in secret sharing"

Connected through hyperedge HE-8 (induced by source paper "polar lattices for lossy compression"):
  with members:
    - "some goodness properties of lda lattices"
    - "lossy compression via sparse linear regression performance under minimum distance encoding"
    - "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"

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)