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 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.

Includes members:
  Member "lossy compression via sparse linear regression performance under minimum distance encoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse regression codes")
  Member "sparse regression codes for multi terminal source and channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse regression codes")
  Member "fast sparse superposition codes have exponentially small error probability for r l c":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "construction of capacity achieving lattice codes polar lattices")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite sample analysis of image registration")
  Member "the dynamics of message passing on dense graphs with applications to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models")

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)