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 "insense incoherent sensor selection for sparse signals"):
  Abstract: Sensor selection refers to the problem of intelligently selecting a small subset of a collection of available sensors to reduce the sensing cost while preserving signal acquisition performance. The majority of sensor selection algorithms find the subset of sensors that best recovers an arbitrary signal from a number of linear measurements that is larger than the dimension of the signal. In this paper, we develop a new sensor selection algorithm for sparse (or near sparse) signals that finds a subset of sensors that best recovers such signals from a number of measurements that is much smaller than the dimension of the signal. Existing sensor selection algorithms cannot be applied in such situations. Our proposed Incoherent Sensor Selection (Insense) algorithm minimizes a coherence-based cost function that is adapted from recent results in sparse recovery theory. Using six datasets, including two real-world datasets on microbial diagnostics and structural health monitoring, we demonstrate the superior performance of Insense for sparse-signal sensor selection.

Includes members:
  Member "near optimal sensor placement for linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "greedy sampling of graph signals")
  Member "projection onto the probability simplex an efficient algorithm with a simple proof and an application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "sparsity promoting sensor selection for non linear measurement models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph sampling for covariance estimation")
  Member "exact recovery conditions for sparse representations with partial support information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an optimal restricted isometry condition for exact sparse recovery with orthogonal least squares")
  Member "online distributed sensor selection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "periodic bandits and wireless network selection")
  Member "deterministic construction of binary bipolar and ternary compressed sensing matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deterministic sparse fourier transform with an ell_infty guarantee")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")

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.OH(Other Computer Science)