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 "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing"):
  Abstract: There has been a growing interest in wideband spectrum sensing due to its applications in cognitive radios and electronic surveillance. To overcome the sampling rate bottleneck for wideband spectrum sensing, in this paper, we study the problem of compressed power spectrum estimation whose objective is to reconstruct the power spectrum of a wide-sense stationary signal based on sub-Nyquist samples. By exploring the sampling structure inherent in the multicoset sampling scheme, we develop a computationally efficient method for power spectrum reconstruction. An important advantage of our proposed method over existing compressed power spectrum estimation methods is that our proposed method, whose primary computational task consists of fast Fourier transform (FFT), has a very low computational complexity. Such a merit makes it possible to efficiently implement the proposed algorithm in a practical field-programmable gate array (FPGA)-based system for real-time wideband spectrum sensing. Our proposed method also provides a new perspective on the power spectrum recovery condition, which leads to a result similar to what was reported in prior works. Simulation results are presented to show the computational efficiency and the effectiveness of the proposed method.

Includes members:
  Member "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed wideband spectrum sensing concept challenges and enablers")
  Member "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analog to digital cognitive radio sampling detection and hardware")
  Member "wideband spectrum sensing for cognitive radio networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "wideband spectrum sensing with sub nyquist sampling in cognitive radios":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an active sequential xampling detector for spectrum sensing")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image guided depth sampling and reconstruction")

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)