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: "random subsets of structured deterministic frames have manova spectra"
  Abstract: We draw a random subset of $k$ rows from a frame with $n$ rows (vectors) and $m$ columns (dimensions), where $k$ and $m$ are proportional to $n$. For a variety of important deterministic equiangular tight frames (ETFs) and tight non-ETF frames, we consider the distribution of singular values of the $k$-subset matrix. We observe that for large $n$ they can be precisely described by a known probability distribution -- Wachter's MANOVA spectral distribution, a phenomenon that was previously known only for two types of random frames. In terms of convergence to this limit, the $k$-subset matrix from all these frames is shown to behave exactly like the classical MANOVA (Jacobi) random matrix ensemble. Thus empirically the MANOVA ensemble offers a universal description of spectra of randomly selected $k$-subframes, even those taken from deterministic frames. The same universality phenomena is shown to hold for notable random frames as well. This description enables exact calculations of properties of solutions for systems of linear equations based on a random choice of $k$ frame vectors out of $n$ possible vectors, and has a variety of implications for erasure coding, compressed sensing, and sparse recovery. Our results are empirical, but they are exhaustive, precise and fully reproducible.

Connected through hyperedge HE-1 (induced by source paper "derandomized compressed sensing with nonuniform guarantees for 120001 1 recovery"):
  with members:
    - "kesten mckay law for random subensembles of paley equiangular tight frames"
    - "average case hardness of rip certification"
    - "the restricted isometry property of subsampled fourier matrices"
    - "living on the edge phase transitions in convex programs with random data"
    - "compressed sensing with sparse binary matrices instance optimal error guarantees in near optimal time"
    - "kirkman equiangular tight frames and codes"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"

Connected through hyperedge HE-2 (induced by source paper "kesten mckay law for random subensembles of paley equiangular tight frames"):
  with members:
    - "fingerprinting with equiangular tight frames"

Connected through hyperedge HE-3 (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-4 (induced by source paper "frame moments and welch bound with erasures"):
  with members:
    - "analog coding of a source with erasures"
    - "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"

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)