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: "restricted isometries for partial random circulant matrices"
  Abstract: In the theory of compressed sensing, restricted isometry analysis has become a standard tool for studying how efficiently a measurement matrix acquires information about sparse and compressible signals. Many recovery algorithms are known to succeed when the restricted isometry constants of the sampling matrix are small. Many potential applications of compressed sensing involve a data-acquisition process that proceeds by convolution with a random pulse followed by (nonrandom) subsampling. At present, the theoretical analysis of this measurement technique is lacking. This paper demonstrates that the $s$th order restricted isometry constant is small when the number $m$ of samples satisfies $m \gtrsim (s \log n)^{3/2}$, where $n$ is the length of the pulse. This bound improves on previous estimates, which exhibit quadratic scaling.

Connected through hyperedge HE-1 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond"):
  with members:
    - "structured linear contextual bandits a sharp and geometric smoothed analysis"
    - "mitigating bias in adaptive data gathering via differential privacy"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "leveraged volume sampling for linear regression"
    - "subsampling for ridge regression via regularized volume sampling"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "structured compressed sensing from theory to applications"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-2 (induced by source paper "unraveling the veil of subspace rip through near isometry on subspaces"):
  with members:
    - "compressed subspace learning based on canonical angle preserving property"
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "robust subspace clustering"
    - "low rank approximation and regression in input sparsity time"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "deterministic partial binary circulant compressed sensing matrices"):
  with members:
    - "quantization of compressive samples with stable and robust recovery"
    - "convolutional compressed sensing using deterministic sequences"
    - "circulant and toeplitz matrices in compressed sensing"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"

Connected through hyperedge HE-4 (induced by source paper "compressive spectrum sensing for cognitive radio networks"):
  with members:
    - "techniques for dealing with uncertainty in cognitive radio networks"
    - "double detector for sparse signal detection from one bit compressed sensing measurements"
    - "one bit compressive sensing of dictionary sparse signals"
    - "a survey of sparse representation algorithms and applications"
    - "spectrum occupancy measurement an autocorrelation based scanning technique using usrp"
    - "matched filter detection with dynamic threshold for cognitive radio networks"
    - "a bayesian network model of the bit error rate for cognitive radio networks"
    - "dynamic spectrum access in cognitive radio networks with rf energy harvesting"

Connected through hyperedge HE-5 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "near optimal sample complexity bounds for circulant binary embedding"

Connected through hyperedge HE-6 (induced by source paper "multispectral compressive imaging strategies using fabry p erot filtered sensors"):
  with members:
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "self calibration and biconvex compressive sensing"
    - "generalized inpainting method for hyperspectral image acquisition"
    - "a parallel compressive imaging architecture for one shot acquisition"

Connected through hyperedge HE-7 (induced by source paper "total variation minimization in compressed sensing"):
  with members:
    - "convex recovery of a structured signal from independent random linear measurements"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "learning without concentration"
    - "structured random measurements in signal processing"
    - "one condition for solution uniqueness and robustness of both l1 synthesis and l1 analysis minimizations"
    - "guarantees of total variation minimization for signal recovery"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"

Connected through hyperedge HE-8 (induced by source paper "quantized compressed sensing for partial random circulant matrices"):
  with members:
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "sigma delta quantization with harmonic frames and partial fourier ensembles"
    - "a deterministic analysis of decimation for sigma delta quantization of bandlimited functions"
    - "quantization of compressive samples with stable and robust recovery"
    - "an rip based approach to sigma delta quantization for compressed sensing"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "one bit compressed sensing by linear programming"

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)