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: "how well can we estimate a sparse vector"
  Abstract: The estimation of a sparse vector in the linear model is a fundamental problem in signal processing, statistics, and compressive sensing. This paper establishes a lower bound on the mean-squared error, which holds regardless of the sensing/design matrix being used and regardless of the estimation procedure. This lower bound very nearly matches the known upper bound one gets by taking a random projection of the sparse vector followed by an $\ell_1$ estimation procedure such as the Dantzig selector. In this sense, compressive sensing techniques cannot essentially be improved.

Connected through hyperedge HE-1 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation"):
  with members:
    - "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "worst case vs average case design for estimation from fixed pairwise comparisons"
    - "an adaptive sublinear time block sparse fourier transform"
    - "lower bounds on active learning for graphical model selection"
    - "f divergence inequalities"
    - "on the difficulty of selecting ising models with approximate recovery"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"

Connected through hyperedge HE-2 (induced by source paper "information theoretic lower bounds for compressive sensing with generative models"):
  with members:
    - "an introductory guide to fano s inequality with applications in statistical estimation"
    - "lower bounds for compressed sensing with generative models"
    - "solving inverse problems via auto encoders"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "the universal approximation power of finite width deep relu networks"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "benefits of depth in neural networks"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "sensitivity of ell_ 1 minimization to parameter choice"):
  with members:
    - "the generalized lasso with non linear observations"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "the squared error of generalized lasso a precise analysis"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "a strong converse bound for multiple hypothesis testing with applications to high dimensional estimation"):
  with members:
    - "the augustin center and the sphere packing bound for memoryless channels"
    - "f divergence inequalities"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "poisson matrix recovery and completion"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "performance bounds for expander based compressed sensing in poisson noise"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "compressed sensing performance bounds under poisson noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "set an algorithm for consistent matrix completion"
    - "matrix completion from noisy entries"
    - "matrix completion from a few entries"

Connected through hyperedge HE-6 (induced by source paper "linear signal recovery from b bit quantized linear measurements precise analysis of the trade off between bit depth and number of measurements"):
  with members:
    - "the generalized lasso with non linear observations"
    - "error decay of almost consistent signal estimations from quantized gaussian random projections"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "constrained adaptive sensing"):
  with members:
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "near optimal adaptive compressed sensing"
    - "nearly optimal sparse fourier transform"
    - "compressive binary search"
    - "stable and robust sampling strategies for compressive imaging"
    - "on the power of adaptivity in sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "exponential decay of reconstruction error from binary measurements of sparse signals"):
  with members:
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"
    - "two part reconstruction in compressed sensing"
    - "sigma delta quantization of sub gaussian frame expansions and its application to compressed sensing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "message passing de quantization with applications to compressed sensing"
    - "one bit compressed sensing with non gaussian measurements"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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)