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 "primary user localization and online radio cartography via structured tensor decomposition"):
  Abstract: Source localization and radio cartography using multi-way representation of spectrum is the subject of study in this paper. A joint matrix factorization and tensor decomposition problem is proposed and solved using an iterative algorithm. The multi-way measured spectrum is organized in a tensor and it is modeled by multiplication of a propagation tensor and a channel gain matrix. The tensor indicates the propagating power from each location and each frequency over time and the channel matrix links the propagating tensor to the sensed spectrum. We utilize sparsity and other intrinsic characteristics of spectrum to identify the solution of the proposed problem. Moreover, The online implementation of the proposed framework results in online radio cartography which is a powerful tool for efficient spectrum awareness and utilization. The simulation results show that our algorithm is a promising technique for dynamic primary user localization and online radio cartography.

Includes members:
  Member "missing spectrum data recovery in cognitive radio networks using piecewise constant nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non intrusive energy disaggregation using non negative matrix factorization with sum to k constraint")
  Member "robustly stable signal recovery in compressed sensing with structured matrix perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast single image dehazing via multilevel wavelet transform based optimization")
  Member "sparsity cognizant total least squares for perturbed compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")

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)