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 limits of sequential testing in high dimensions"
  Abstract: This paper presents results pertaining to sequential methods for support recovery of sparse signals in noise. Specifically, we show that any sequential measurement procedure fails provided the average number of measurements per dimension grows slower then log s / D(f0||f1) where s is the level of sparsity, and D(f0||f1) the Kullback-Leibler divergence between the underlying distributions. For comparison, we show any non-sequential procedure fails provided the number of measurements grows at a rate less than log n / D(f1||f0), where n is the total dimension of the problem. Lastly, we show that a simple procedure termed sequential thresholding guarantees exact support recovery provided the average number of measurements per dimension grows faster than (log s + log log n) / D(f0||f1), a mere additive factor more than the lower bound.

Connected through hyperedge HE-1 (induced by source paper "compressed anomaly detection with multiple mixed observations"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "compressed hypothesis testing to mix or not to mix"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "statistical compressed sensing of gaussian mixture models"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "compressive binary search"
    - "lower bounds for sparse recovery"
    - "information theoretic bounds for compressed sensing"
    - "the price of certainty waterslide curves and the gap to capacity"

Connected through hyperedge HE-3 (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"
    - "sequential testing for sparse recovery"
    - "convex approaches to model wavelet sparsity patterns"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "performance guarantees for adaptive estimation of sparse signals"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "query driven frequent co occurring term extraction over relational data using mapreduce"
    - "adaptive sensing of congested spectrum bands"

Connected through hyperedge HE-5 (induced by source paper "the sample complexity of search over multiple populations"):
  with members:
    - "finding a most biased coin with fewest flips"
    - "adaptive sensing of congested spectrum bands"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "compressed hypothesis testing to mix or not to mix"):
  with members:
    - "quickest search over multiple sequences with mixed observations"
    - "chernoff information of exponential families"
    - "kalman filtered compressed sensing"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

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)