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: "compressive binary search"
  Abstract: In this paper we consider the problem of locating a nonzero entry in a high-dimensional vector from possibly adaptive linear measurements. We consider a recursive bisection method which we dub the compressive binary search and show that it improves on what any nonadaptive method can achieve. We also establish a non-asymptotic lower bound that applies to all methods, regardless of their computational complexity. Combined, these results show that the compressive binary search is within a double logarithmic factor of the optimal performance.

Connected through hyperedge HE-1 (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"
    - "on the limits of sequential testing in high dimensions"
    - "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-2 (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"
    - "stable and robust sampling strategies for compressive imaging"
    - "on the power of adaptivity in sparse recovery"
    - "how well can we estimate a sparse vector"

Connected through hyperedge HE-3 (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"
    - "sketched svd recovering spectral features from compressive measurements"
    - "algorithms and hardness for robust subspace recovery"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-4 (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"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sketching sparse matrices"
    - "communications inspired projection design with application to compressive sensing"
    - "sequential compressed sensing"

Connected through hyperedge HE-5 (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-6 (induced by source paper "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"):
  with members:
    - "near optimal adaptive compressed sensing"
    - "sequential testing for sparse recovery"
    - "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-7 (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"
    - "sequential testing for sparse recovery"
    - "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)