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 "how to find real world applications of compressive sensing"):
  Abstract: The potential of compressive sensing (CS) has spurred great interest in the research community and is a fast#R##N#growing area of research. However, research translating CS theory into practical hardware and demonstrating#R##N#clear and significant benefits with this hardware over current, conventional imaging techniques has been limited.#R##N#This article helps researchers to find those niche applications where the CS approach provide substantial gain#R##N#over conventional approaches by articulating guidelines for finding these niche CS applications. Furthermore, in#R##N#this paper we utilized these guidelines to find one such new application for CS; sea skimming missile detection. As#R##N#a proof of concept, it is demonstrated that a simplified CS missile detection architecture and algorithm provides#R##N#comparable results to the conventional imaging approach but using a smaller FPA.

Includes members:
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "compressive sensing with local geometric features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on low risk heavy hitters and sparse recovery schemes")
  Member "on variable density compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "subspace pursuit for compressive sensing signal reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compressive sensing for cut improvement and local clustering")

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.CV(Computer Vision and Pattern Recognition)