human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "remote sensing via ell_1 minimization"
  Abstract: We consider the problem of detecting the locations of targets in the far field by sending probing signals from an antenna array and recording the reflected echoes. Drawing on key concepts from the area of compressive sensing, we use an $\ell_1$-based regularization approach to solve this, in general ill-posed, inverse scattering problem. As common in compressed sensing, we exploit randomness, which in this context comes from choosing the antenna locations at random. With $n$ antennas we obtain $n^2$ measurements of a vector $x \in \C^{N}$ representing the target locations and reflectivities on a discretized grid. It is common to assume that the scene $x$ is sparse due to a limited number of targets. Under a natural condition on the mesh size of the grid, we show that an $s$-sparse scene can be recovered via $\ell_1$-minimization with high probability if $n^2 \geq C s \log^2(N)$. The reconstruction is stable under noise and under passing from sparse to approximately sparse vectors. Our theoretical findings are confirmed by numerical simulations.

Connected through hyperedge HE-1 (induced by source paper "spatial compressive sensing for mimo radar"):
  with members:
    - "analysis of sparse mimo radar"
    - "sub nyquist radar via doppler focusing"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "a probabilistic and ripless theory of compressed sensing"
    - "mimo radar using compressive sampling"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-2 (induced by source paper "multi branch matching pursuit with applications to mimo radar"):
  with members:
    - "spatial compressive sensing for mimo radar"
    - "analysis of sparse mimo radar"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "mimo radar using compressive sampling"
    - "rank awareness in joint sparse recovery"
    - "message passing algorithms for compressed sensing"
    - "iterative hard thresholding for compressed sensing"

Question: Please tell me which class the center node 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)