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 "constrained adaptive sensing"):
  Abstract: Suppose that we wish to estimate a vector x ∈ C#N# n#N# from a small number of noisy linear measurements of the form y = Ax + z, where z represents measurement noise. When the vector x is sparse, meaning that it has only s nonzeros with s ≪ n, one can obtain a significantly more accurate estimate of x by adaptively selecting the rows of A based on the previous measurements provided that the signal-to-noise ratio is sufficiently large. In this paper, we consider the case where we wish to realize the potential of adaptivity but where the rows of A are subject to physical constraints. In particular, we examine the case where the rows of A are constrained to belong to a finite set of allowable measurement vectors. We demonstrate both the limitations and advantages of adaptive sensing in this constrained setting. We prove that for certain measurement ensembles, the benefits offered by adaptive designs fall far short of the improvements that are possible in the unconstrained adaptive setting. On the other hand, we also provide both theoretical and empirical evidence that in some scenarios, adaptivity does still result in substantial improvements even in the constrained setting. To illustrate these potential gains, we propose practical algorithms for constrained adaptive sensing by exploiting connections to the theory of optimal experimental design and show that these algorithms exhibit promising performance in some representative applications.

Includes members:
  Member "compressive sensing with redundant dictionaries and structured measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications")
  Member "near optimal adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "nearly optimal sparse fourier transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "compressive binary search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing")
  Member "stable and robust sampling strategies for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "on the power of adaptivity in sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "how well can we estimate a sparse vector":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an introductory guide to fano s inequality with applications in statistical estimation")

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)