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: "efficient compressed sensing using lossless expander graphs with fast bilateral quantum recovery algorithm"
  Abstract: Compressed Sensing is a novel approach to bypass the Nyquist sampling limits whenever the signals are sparse, and to compress them simultaneously. In this paper, improving our previous results, we will propose a compressed sensing algorithm based on the high-quality lossless unbalanced vertex expander graphs, with a fast and simple quantum decoding algorithm. Exploiting the unique neighborhood property of the lossless expander graphs in combination with the efficient quantum algorithm for finding distinct elements among a set we will prove the validity and efficiency of the algorithm and will show how the combination of the lossless expander graphs and quantum recovery algorithm leads to a general compressed sensing framework which is as good as the previous results in terms of the sketch size, encoding time, update time, and having explicit construction; furthermore, it is superior to the previous recovery algorithms in both the recovery time and simplicity of implementation. Finally we will show how the algorithm can be modified to be robust for a well-structured family of almost sparse signals. The robust algorithm will first find the position of the largest elements of the signal, and then using this information finds efficiently an explicit sparse approximation for the original signal.

Connected through hyperedge HE-1 (induced by source paper "deterministic compressed sensing matrices from multiplicative character sequences"):
  with members:
    - "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"
    - "reed muller sensing matrices and the lasso"
    - "deterministic compressed sensing matrices from additive character sequences"
    - "the sparsity gap uncertainty principles proportional to dimension"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "a sublinear algorithm for sparse reconstruction with l2 l2 recovery guarantees"

Connected through hyperedge HE-2 (induced by source paper "deterministic construction of partial fourier compressed sensing matrices via cyclic difference sets"):
  with members:
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"

Connected through hyperedge HE-3 (induced by source paper "statistical rip and semi circle distribution of incoherent dictionaries"):
  with members:
    - "the finite harmonic oscillator and its associated sequences"
    - "deterministic designs with deterministic guarantees toeplitz compressed sensing matrices sequence designs and system identification"
    - "on some deterministic dictionaries supporting sparsity"
    - "the finite harmonic oscillator and its applications to sequences communication and radar"
    - "on the diagonalization of the discrete fourier transform"

Connected through hyperedge HE-4 (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"
    - "deterministic designs with deterministic guarantees toeplitz compressed sensing matrices sequence designs and system identification"
    - "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"
    - "on the diagonalization of the discrete fourier transform"

Connected through hyperedge HE-5 (induced by source paper "incoherent dictionaries and the statistical restricted isometry property"):
  with members:
    - "the finite harmonic oscillator and its associated sequences"
    - "deterministic designs with deterministic guarantees toeplitz compressed sensing matrices sequence designs and system identification"
    - "on some deterministic dictionaries supporting sparsity"
    - "the finite harmonic oscillator and its applications to sequences communication and radar"

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)