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 "reconstruction of frequency hopping signals from multi coset samples"):
  Abstract: Multi-Coset (MC) sampling is a well established, practically feasible scheme for sampling multiband analog signals below the Nyquist rate. MC sampling has gained renewed interest in the Compressive Sensing (CS) community, due partly to the fact that in the frequency domain, MC sampling bears a strong resemblance to other sub-Nyquist CS acquisition protocols. In this paper, we consider MC sampling of analog frequency hopping signals, which can be viewed as multiband signals with changing band positions. This nonstationarity motivates our consideration of a segment-based reconstruction framework, in which the sample stream is broken into short segments for reconstruction. In contrast, previous works focusing on the reconstruction of multiband signals have used a segment-less reconstruction framework such as the modified MUSIC algorithm. We outline the challenges associated with segment-based recovery of frequency hopping signals from MC samples, and we explain how these challenges can be addressed using conventional CS recovery techniques. We also demonstrate the utility of the Discrete Prolate Spheroidal Sequences (DPSS's) as an efficient dictionary for reducing the computational complexity of segment-based reconstruction.

Includes members:
  Member "compressive sensing of analog signals using discrete prolate spheroidal sequences":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")

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)