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 "multi dimensional spectral super resolution with prior knowledge via frequency selective vandermonde decomposition and admm"):
  Abstract: This paper is concerned with estimation of multiple frequencies from incomplete and/or noisy samples based on a low-CP-rank tensor data model where each CP vector is an array response vector of one frequency. Suppose that it is known a priori that the frequencies lie in some given intervals, we develop efficient super-resolution estimators by exploiting such prior knowledge based on frequency-selective (FS) atomic norm minimization. We study the MD Vandermonde decomposition of block Toeplitz matrices in which the frequencies are restricted to lie in given intervals. We then propose to solve the FS atomic norm minimization problems for the low-rank spectral tensor recovery by converting them into semidefinite programs based on the MD Vandermonde decomposition. We also develop fast solvers for solving these semidefinite programs via the alternating direction method of multipliers (ADMM), where each iteration involves a number of refinement steps to utilize the prior knowledge. Extensive simulation results are presented to illustrate the high performance of the proposed methods.

Includes members:
  Member "multi target position and velocity estimation using ofdm communication signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive multidimensional harmonic retrieval with prior knowledge")
  Member "adaptive interference removal for uncoordinated radar communication coexistence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interference removal for radar communication co existence the random scattering case")
  Member "recovery of structured signals with prior information via maximizing correlation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "near optimal sample complexity for convex tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms")
  Member "millimeter wave beamformed full dimensional mimo channel estimation based on atomic norm minimization":
    also connected through other hyperedges:
      (none)
  Member "super resolution delay doppler estimation for ofdm passive radar":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressive multidimensional harmonic retrieval with prior knowledge")
  Member "rank determination for low rank data completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns")
  Member "characterization of deterministic and probabilistic sampling patterns for finite completability of low tensor train rank tensor":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on deterministic sampling patterns for robust low rank matrix completion")

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)