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 "spatial compressive sensing for mimo radar"):
  Abstract: We study compressive sensing in the spatial domain to achieve target localization, specifically direction of arrival (DOA), using multiple-input multiple-output (MIMO) radar. A sparse localization framework is proposed for a MIMO array in which transmit and receive elements are placed at random. This allows for a dramatic reduction in the number of elements needed, while still attaining performance comparable to that of a filled (Nyquist) array. By leveraging properties of structured random matrices, we develop a bound on the coherence of the resulting measurement matrix, and obtain conditions under which the measurement matrix satisfies the so-called isotropy property. The coherence and isotropy concepts are used to establish uniform and non-uniform recovery guarantees within the proposed spatial compressive sensing framework. In particular, we show that non-uniform recovery is guaranteed if the product of the number of transmit and receive elements, MN (which is also the number of degrees of freedom), scales with K(logG)2, where K is the number of targets and G is proportional to the array aperture and determines the angle resolution. In contrast with a filled virtual MIMO array where the product MN scales linearly with G, the logarithmic dependence on G in the proposed framework supports the high-resolution provided by the virtual array aperture while using a small number of MIMO radar elements. In the numerical results we show that, in the proposed framework, compressive sensing recovery algorithms are capable of better performance than classical methods, such as beamforming and MUSIC.

Includes members:
  Member "analysis of sparse mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super resolution radar imaging via convex optimization")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "remote sensing via ell_1 minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi branch matching pursuit with applications to mimo radar")
  Member "identification of parametric underspread linear systems and super resolution radar":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a general and yet efficient scheme for sub nyquist radar processing")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "mimo radar using compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "time delay estimation from low rate samples a union of subspaces approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "rank awareness in joint sparse recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)