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 "improving compressed sensing with the diamond norm"):
  Abstract: In low-rank matrix recovery, one aims to reconstruct a low-rank matrix from a minimal number of linear measurements. Within the paradigm of compressed sensing, this is made computationally efficient by minimizing the nuclear norm as a convex surrogate for rank. In this paper, we identify an improved regularizer based on the so-called diamond norm, a concept imported from quantum information theory. We show that—for a class of matrices saturating a certain norm inequality—the descent cone of the diamond norm is contained in that of the nuclear norm. This suggests superior reconstruction properties for these matrices. We explicitly characterize this set of matrices. Moreover, we demonstrate numerically that the diamond norm indeed outperforms the nuclear norm in a number of relevant applications: These include signal analysis tasks, such as blind matrix deconvolution or the retrieval of certain unitary basis changes, as well as the quantum information problem of process tomography with random measurements. The diamond norm is defined for matrices that can be interpreted as order-4 tensors and it turns out that the above condition depends crucially on that tensorial structure. In this sense, this paper touches on an aspect of the notoriously difficult tensor completion problem.

Includes members:
  Member "comments on improving compressed sensing with the diamond norm saturation of the norm inequalities between diamond and nuclear norm":
    also connected through other hyperedges:
      (none)
  Member "improved recovery guarantees for phase retrieval from coded diffraction patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "tensor theta norms and low rank recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simultaneous structures in convex signal recovery revisiting the convex combination of norms")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "a partial derandomization of phaselift using spherical designs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complex phase retrieval from subgaussian measurements")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing with 1d total variation breaking sample complexity barriers via non uniform recovery")
  Member "learning without concentration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")

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)