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: "sequential testing for sparse recovery"
  Abstract: This paper studies sequential methods for recovery of sparse signals in high dimensions. When compared to fixed sample size procedures, in the sparse setting, sequential methods can result in a large reduction in the number of samples needed for reliable signal support recovery. Starting with a lower bound, we show any coordinate-wise sequential sampling procedure fails in the high dimensional limit provided the average number of measurements per dimension is less then log s/D(P_0||P_1) where s is the level of sparsity and D(P_0||P_1) the Kullback-Leibler divergence between the underlying distributions. A series of Sequential Probability Ratio Tests (SPRT) which require complete knowledge of the underlying distributions is shown to achieve this bound. Motivated by real world experiments and recent work in adaptive sensing, we introduce a simple procedure termed Sequential Thresholding which can be implemented when the underlying testing problem satisfies a monotone likelihood ratio assumption. Sequential Thresholding guarantees exact support recovery provided the average number of measurements per dimension grows faster than log s/ D(P_0||P_1), achieving the lower bound. For comparison, we show any non-sequential procedure fails provided the number of measurements grows at a rate less than log n/D(P_1||P_0), where n is the total dimension of the problem.

Connected through hyperedge HE-1 (induced by source paper "searching for anomalies over composite hypotheses"):
  with members:
    - "active anomaly detection in heterogeneous processes"
    - "learning to detect an oddball target"
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "asymptotically optimal anomaly detection via sequential testing"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

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"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "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"

Connected through hyperedge HE-3 (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"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "asymptotically optimal anomaly detection via sequential testing"):
  with members:
    - "optimal index policies for anomaly localization in resource constrained cyber systems"
    - "active hypothesis testing for quickest anomaly detection"
    - "universal outlier hypothesis testing"
    - "quickest search over multiple sequences with mixed observations"
    - "active sequential hypothesis testing"
    - "sequentiality and adaptivity gains in active hypothesis testing"
    - "controlled sensing for multihypothesis testing"

Connected through hyperedge HE-5 (induced by source paper "resource constrained adaptive search for sparse multi class targets with varying importance"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "on the power of adaptivity in sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "resource constrained adaptive search and tracking for sparse dynamic targets"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "performance guarantees for adaptive estimation of sparse signals"

Connected through hyperedge HE-7 (induced by source paper "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "compressive binary search"
    - "structured compressed sensing from theory to applications"
    - "on the power of adaptivity in sparse recovery"
    - "sharp support recovery from noisy random measurements by l1 minimization"
    - "how well can we estimate a sparse vector"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "compressive imaging using approximate message passing and a markov tree prior"

Connected through hyperedge HE-8 (induced by source paper "near optimal adaptive compressed sensing"):
  with members:
    - "the sample complexity of search over multiple populations"
    - "near optimal compressive binary search"
    - "boolean compressed sensing and noisy group testing"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "convex approaches to model wavelet sparsity patterns"
    - "on the limits of sequential testing in high dimensions"
    - "information theoretic bounds for compressed sensing"

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)