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: "asymptotic behaviour of random vandermonde matrices with entries on the unit circle"
  Abstract: Analytical methods for finding moments of random Vandermonde matrices with entries on the unit circle are developed. Vandermonde Matrices play an important role in signal processing and wireless applications such as direction of arrival estimation, precoding, and sparse sampling theory, just to name a few. Within this framework, we extend classical freeness results on random matrices with independent, identically distributed (i.i.d.) entries and show that Vandermonde structured matrices can be treated in the same vein with different tools. We focus on various types of matrices, such as Vandermonde matrices with and without uniform phase distributions, as well as generalized Vandermonde matrices. In each case, we provide explicit expressions of the moments of the associated Gram matrix, as well as more advanced models involving the Vandermonde matrix. Comparisons with classical i.i.d. random matrix theory are provided, and deconvolution results are discussed. We review some applications of the results to the fields of signal processing and wireless communications.

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 "random subsets of structured deterministic frames have manova spectra"):
  with members:
    - "analog coding of a source with erasures"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "spatial degrees of freedom of mimo systems in line of sight environment"):
  with members:
    - "capacity scaling of wireless ad hoc networks shannon meets maxwell"
    - "reconstruction of multidimensional signals from irregular noisy samples"

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)