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 "info greedy sequential adaptive compressed sensing"):
  Abstract: We present an information-theoretic framework for sequential adaptive compressed sensing, Info-Greedy Sensing, where measurements are chosen to maximize the extracted information conditioned on the previous measurements. We show that the widely used bisection approach is Info-Greedy for a family of $k$-sparse signals by connecting compressed sensing and blackbox complexity of sequential query algorithms, and present Info-Greedy algorithms for Gaussian and Gaussian Mixture Model (GMM) signals, as well as ways to design sparse Info-Greedy measurements. Numerical examples demonstrate the good performance of the proposed algorithms using simulated and real data: Info-Greedy Sensing shows significant improvement over random projection for signals with sparse and low-rank covariance matrices, and adaptivity brings robustness when there is a mismatch between the assumed and the true distributions.

Includes members:
  Member "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilinear compressive learning with prior knowledge")
  Member "information theoretic bounds for adaptive sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure adaptive group testing")
  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 "distance based and continuum fano inequalities with applications to statistical estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")
  Member "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "leveraging union of subspace structure to improve constrained clustering")
  Member "performance guarantees for adaptive estimation of sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improved active sensing performance in wireless sensor networks via channel state information extended version")
  Member "communications inspired projection design with application to compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "measurement matrix design for phase retrieval based on mutual information")

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)