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 "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.

Includes members:
  Member "analog coding of a source with erasures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the relationship between dropout and equiangular tight frames")
  Member "the restricted isometry property of subsampled fourier matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "restricted isometry of fourier matrices and list decodability of random linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one shot neural architecture search via compressive sensing")
  Member "new constructions of rip matrices with fast multiplication and fewer rows":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data")
  Member "the restricted isometry property for time frequency structured random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices")
  Member "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "frame moments and welch bound with erasures")
  Member "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "near optimal binary compressed sensing matrix")
  Member "asymptotic behaviour of random vandermonde matrices with entries on the unit circle":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the relationship between dropout and equiangular tight frames")

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)