human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sequential information guided sensing"):
  Abstract: We study the value of information in sequential compressed sensing by characterizing the performance of sequential information guided sensing in practical scenarios when information is inaccurate. In particular, we assume the signal distribution is parameterized through Gaussian or Gaussian mixtures with estimated mean and covariance matrices, and we can measure compressively through a noisy linear projection or using one-sparse vectors, i.e., observing one entry of the signal each time. We establish a set of performance bounds for the bias and variance of the signal estimator via posterior mean, by capturing the conditional entropy (which is also related to the size of the uncertainty), and the additional power required due to inaccurate information to reach a desired precision. Based on this, we further study how to estimate covariance based on direct samples or covariance sketching. Numerical examples also demonstrate the superior performance of Info-Greedy Sensing algorithms compared with their random and non-adaptive counterparts.

Includes members:
  Member "info greedy sequential adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an active sequential xampling detector for spectrum sensing")
  Member "exact and stable covariance estimation from quadratic sampling via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "near optimal adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "task driven adaptive statistical compressive sensing of gaussian mixture models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information")
  Member "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "leveraging union of subspace structure to improve constrained clustering")
  Member "sketching sparse matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical restricted isometry property for kronecker product measurements")
  Member "communications inspired projection design with application to compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "measurement matrix design for phase retrieval based on mutual information")
  Member "compressive binary search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing")

Question: Please tell me which class the source paper 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)