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 "branchhull convex bilinear inversion from the entrywise product of signals with known signs"):
  Abstract: We consider the bilinear inverse problem of recovering two vectors, $x$ and $w$, in $\mathbb{R}^L$ from their entrywise product. For the case where the vectors have known signs and belong to known subspaces, we introduce the convex program BranchHull, which is posed in the natural parameter space that does not require an approximate solution or initialization in order to be stated or solved. Under the structural assumptions that $x$ and $w$ are members of known $K$ and $N$ dimensional random subspaces, we present a recovery guarantee for the noiseless case and a noisy case. In the noiseless case, we prove that the BranchHull recovers $x$ and $w$ up to the inherent scaling ambiguity with high probability when $L\ \gg\ 2(K+N)$. The analysis provides a precise upper bound on the coefficient for the sample complexity. In a noisy case, we show that with high probability the BranchHull is robust to small dense noise when $L = \Omega(K+N)$. BranchHull is motivated by the sweep distortion removal task in dielectric imaging, where one of the signals is a nonnegative reflectivity, and the other signal lives in a known wavelet subspace. Additional potential applications are blind deconvolution and self-calibration.

Includes members:
  Member "corruption robust phase retrieval via linear programming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving equations of random convex functions via anchored regression")
  Member "phasemax convex phase retrieval via basis pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "rapid robust and reliable blind deconvolution via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "leveraging diversity and sparsity in blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "a geometric analysis of phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "phase retrieval meets statistical learning theory a flexible convex relaxation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)