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 "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"):
  Abstract: We investigate recovery of nonnegative vectors from non-adaptive compressive measurements in the presence of noise of unknown power. In the absence of noise, existing results in the literature identify properties of the measurement that assure uniqueness in the non-negative orthant. By linking such uniqueness results to nullspace properties, we deduce uniform and robust compressed sensing guarantees for nonnegative least squares. No l1-regularization is required. As an important proof of principle, we establish that m x n random i.i.d. 0/1-valued Bernoulli matrices obey the required conditions with overwhelming probability provided that m=O(slog(n/s)). We achieve this by establishing the robust nullspace property for random 0/1-matrices - a novel result in its own right. Our analysis is motivated by applications in wireless network activity detection.

Includes members:
  Member "stable low rank matrix recovery via null space properties":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "on the gap between rip properties and sparse recovery conditions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver")
  Member "convex recovery of a structured signal from independent random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "learning with correntropy induced losses for regression with mixture of symmetric stable noise")
  Member "an empirical bayes approach to recovering linearly constrained non negative sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning and free energies for vector approximate message passing")
  Member "low rank matrix recovery from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "ripless compressed sensing from anisotropic measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "infinite dimensional compressed sensing from anisotropic measurements and applications to inverse problems in pde")
  Member "phaselift exact and stable signal recovery from magnitude measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")

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)