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 "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  Abstract: The realisation of sensing modalities based on the principles of compressed sensing is often hindered by discrepancies between the mathematical model of its sensing operator, which is necessary during signal recovery, and its actual physical implementation, which can amply differ from the assumed model. In this paper we tackle the bilinear inverse problem of recovering a sparse input signal and some unknown, unstructured multiplicative factors affecting the sensors that capture each compressive measurement. Our methodology relies on collecting a few snapshots under new draws of the sensing operator, and applying a greedy algorithm based on projected gradient descent and the principles of iterative hard thresholding. We explore empirically the sample complexity requirements of this algorithm by testing its phase transition, and show in a practically relevant instance of this problem for compressive imaging that the exact solution can be obtained with only a few snapshots.

Includes members:
  Member "a non convex blind calibration method for randomised sensing strategies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging")
  Member "self calibration and bilinear inverse problems via linear least squares":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "empirical chaos processes and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self calibration and bilinear inverse problems via linear least squares")
  Member "rapid robust and reliable blind deconvolution via nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "through the haze a non convex approach to blind gain calibration for linear random sensing models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem")
  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 "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "low complexity multiclass encryption by compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")

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)