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: "tensor theta norms and low rank recovery"
  Abstract: We study extensions of compressive sensing and low rank matrix recovery to the recovery of tensors of low rank from incomplete linear information. While the reconstruction of low rank matrices via nuclear norm minimization is rather well-understand by now, almost no theory is available so far for the extension to higher order tensors due to various theoret- ical and computational difficulties arising for tensor decompositions. In fact, nuclear norm minimization for matrix recovery is a tractable convex relaxation approach, but the extension of the nuclear norm to tensors is NP-hard to compute. In this article, we introduce convex relaxations of the tensor nuclear norm which are computable in polynomial time via semidef- inite programming. Our approach is based on theta bodies, a concept from computational algebraic geometry which is similar to the one of the better known Lasserre relaxations. We introduce polynomial ideals which are generated by the second order minors corresponding to different matricizations of the tensor (where the tensor entries are treated as variables) such that the nuclear norm ball is the convex hull of the algebraic variety of the ideal. The theta body of order k for such an ideal generates a new norm which we call thek-norm. We show that in the matrix case, these norms reduce to the standard nuclear norm. For tensors of order three or higher however, we indeed obtain new norms. By providing the Grobner basis for the ideals, we explicitly give semidefinite programs for the computation of thek-norm and for the minimization of thek-norm under an affine constraint. Finally, numerical experiments for order three tensor recovery via �1-norm minimization suggest that our approach successfully reconstructs tensors of low rank from incomplete linear (random) measurements.

Connected through hyperedge HE-1 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms"):
  with members:
    - "sparse power factorization balancing peakiness and sample complexity"
    - "near optimal sample complexity for convex tensor completion"
    - "branchhull convex bilinear inversion from the entrywise product of signals with known signs"
    - "reliable recovery of hierarchically sparse signals and application in machine type communications"
    - "low rank tensor recovery via iterative hard thresholding"
    - "self calibration and biconvex compressive sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sparse signal processing concepts for efficient 5g system design"

Connected through hyperedge HE-2 (induced by source paper "improving compressed sensing with the diamond norm"):
  with members:
    - "comments on improving compressed sensing with the diamond norm saturation of the norm inequalities between diamond and nuclear norm"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "self calibration and biconvex compressive sensing"
    - "stable low rank matrix recovery via null space properties"
    - "a partial derandomization of phaselift using spherical designs"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"

Connected through hyperedge HE-3 (induced by source paper "low rank tensor recovery via iterative hard thresholding"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "stable low rank matrix recovery via null space properties"
    - "tensor prediction rademacher complexity and random 3 xor"
    - "approximation algorithms for model based compressive sensing"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "structured random measurements in signal processing"
    - "low rank matrix recovery from rank one measurements"
    - "compressive multiplexing of correlated signals"

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)