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: "deterministic designs with deterministic guarantees toeplitz compressed sensing matrices sequence designs and system identification"
  Abstract: In this paper we present a new family of discrete sequences having "random like" uniformly decaying auto-correlation properties. The new class of infinite length sequences are higher order chirps constructed using irrational numbers. Exploiting results from the theory of continued fractions and diophantine approximations, we show that the class of sequences so formed has the property that the worst-case auto-correlation coefficients for every finite length sequence decays at a polynomial rate. These sequences display doppler immunity as well. We also show that Toeplitz matrices formed from such sequences satisfy restricted-isometry-property (RIP), a concept that has played a central role recently in Compressed Sensing applications. Compressed sensing has conventionally dealt with sensing matrices with arbitrary components. Nevertheless, such arbitrary sensing matrices are not appropriate for linear system identification and one must employ Toeplitz structured sensing matrices. Linear system identification plays a central role in a wide variety of applications such as channel estimation for multipath wireless systems as well as control system applications. Toeplitz matrices are also desirable on account of their filtering structure, which allows for fast implementation together with reduced storage requirements.

Connected through hyperedge HE-1 (induced by source paper "state of the art and prospects of structured sensing matrices in compressed sensing"):
  with members:
    - "convolutional compressed sensing using deterministic sequences"
    - "projection design for statistical compressive sensing a tight frame based approach"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "structured compressed sensing from theory to applications"
    - "a probabilistic and ripless theory of compressed sensing"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "deterministic compressed sensing matrices from multiplicative character sequences"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing matrices from fourier matrices"):
  with members:
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "the performance of orthogonal multi matching pursuit under rip"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-3 (induced by source paper "information theoretic bounds for compressed sensing"):
  with members:
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "bayesian compressive sensing via belief propagation"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-4 (induced by source paper "orthogonal symmetric toeplitz matrices for compressed sensing statistical isometry property"):
  with members:
    - "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-5 (induced by source paper "statistical rip and semi circle distribution of incoherent dictionaries"):
  with members:
    - "the finite harmonic oscillator and its associated sequences"
    - "on some deterministic dictionaries supporting sparsity"
    - "the finite harmonic oscillator and its applications to sequences communication and radar"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"
    - "on the diagonalization of the discrete fourier transform"

Connected through hyperedge HE-6 (induced by source paper "limits of deterministic compressed sensing considering arbitrary orthonormal basis for sparsity"):
  with members:
    - "uncertainty relations for shift invariant analog signals"
    - "algorithmic linear dimension reduction in the l_1 norm for sparse vectors"

Connected through hyperedge HE-7 (induced by source paper "linear transformations and restricted isometry property"):
  with members:
    - "on some deterministic dictionaries supporting sparsity"

Connected through hyperedge HE-8 (induced by source paper "the statistical restricted isometry property and the wigner semicircle distribution of incoherent dictionaries"):
  with members:
    - "incoherent dictionaries and the statistical restricted isometry property"
    - "the finite harmonic oscillator and its associated sequences"
    - "efficient and robust compressed sensing using high quality expander graphs"
    - "on some deterministic dictionaries supporting sparsity"
    - "the finite harmonic oscillator and its applications to sequences communication and radar"
    - "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"
    - "on the diagonalization of the discrete fourier transform"

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)