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: "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
  Abstract: Recent breakthrough results in compressive sensing (CS) have established that many high dimensional signals can be accurately recovered from a relatively small number of non-adaptive linear observations, provided that the signals possess a sparse representation in some basis. Subsequent efforts have shown that the performance of CS can be improved by exploiting additional structure in the locations of the nonzero signal coefficients during inference, or by utilizing some form of data-dependent adaptive measurement focusing during the sensing process. To our knowledge, our own previous work was the first to establish the potential benefits that can be achieved when fusing the notions of adaptive sensing and structured sparsity -- that work examined the task of support recovery from noisy linear measurements, and established that an adaptive sensing strategy specifically tailored to signals that are tree-sparse can significantly outperform adaptive and non-adaptive sensing strategies that are agnostic to the underlying structure. In this work we establish fundamental performance limits for the task of support recovery of tree-sparse signals from noisy measurements, in settings where measurements may be obtained either non-adaptively (using a randomized Gaussian measurement strategy motivated by initial CS investigations) or by any adaptive sensing strategy. Our main results here imply that the adaptive tree sensing procedure analyzed in our previous work is nearly optimal, in the sense that no other sensing and estimation strategy can perform fundamentally better for identifying the support of tree-sparse signals.

Connected through hyperedge HE-1 (induced by source paper "leveraging union of subspace structure to improve constrained clustering"):
  with members:
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust subspace clustering"
    - "active clustering with model based uncertainty reduction"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "on the power of adaptivity in sparse recovery"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "compressive principal component pursuit"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "sketched svd recovering spectral features from compressive measurements"
    - "algorithms and hardness for robust subspace recovery"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-3 (induced by source paper "sequential information guided sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "sketching sparse matrices"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"
    - "sequential compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "info greedy sequential adaptive compressed sensing"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"

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)