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: "analog coding of a source with erasures"
  Abstract: Analog coding decouples the tasks of protecting against erasures and noise. For erasure correction, it creates an "analog redundancy" by means of band-limited discrete Fourier transform (DFT) interpolation, or more generally, by an over-complete expansion based on a frame. We examine the analog coding paradigm for the dual setup of a source with "erasure" side-information (SI) at the encoder. The excess rate of analog coding above the rate-distortion function (RDF) is associated with the energy of the inverse of submatrices of the frame, where each submatrix corresponds to a possible erasure pattern. We give a partial theoretical as well as numerical evidence that a variety of structured frames, in particular DFT frames with difference-set spectrum and more general equiangular tight frames (ETFs), with a common MANOVA limiting spectrum, minimize the excess rate over all possible frames. However, they do not achieve the RDF even in the limit as the dimension goes to infinity.

Connected through hyperedge HE-1 (induced by source paper "on the relationship between dropout and equiangular tight frames"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "on the implicit bias of dropout"
    - "deep metric learning with hierarchical triplet loss"
    - "dropout is a special case of the stochastic delta rule faster and more accurate deep learning"
    - "frame moments and welch bound with erasures"
    - "focal loss for dense object detection"
    - "learning deep networks from noisy labels with dropout regularization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "analog coding frame work"):
  with members:
    - "frame moments and welch bound with erasures"
    - "sampling versus random binning for multiple descriptions of a bandlimited source"
    - "the restricted isometry property for time frequency structured random matrices"
    - "incoherent dictionaries and the statistical restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "frame moments and welch bound with erasures"):
  with members:
    - "random subsets of structured deterministic frames have manova spectra"
    - "sampling versus random binning for multiple descriptions of a bandlimited source"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-4 (induced by source paper "lattice erasure codes of low rank with noise margins"):
  with members:
    - "graph based random access for the collision channel without feedback capacity bound"
    - "unitary precoding and basis dependency of mmse performance for gaussian erasure channels"

Connected through hyperedge HE-5 (induced by source paper "coding method for parallel iterative linear solver"):
  with members:
    - "speeding up distributed machine learning using codes"
    - "coded computation over heterogeneous clusters"
    - "polynomial codes an optimal design for high dimensional coded matrix multiplication"
    - "coding for distributed fog computing"
    - "short dot computing large linear transforms distributedly using coded short dot products"
    - "on the worst case communication overhead for distributed data shuffling"
    - "local set based graph signal reconstruction"
    - "discrete signal processing on graphs sampling theory"

Connected through hyperedge HE-6 (induced by source paper "random subsets of structured deterministic frames have manova spectra"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "the restricted isometry property for time frequency structured random matrices"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"
    - "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"

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)