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 "compressive phase retrieval of sparse bandlimited signals"):
  Abstract: This contribution proposes a two stage strategy to allow for phase retrieval in state of the art sub-Nyquist sampling schemes for sparse multiband signals. The proposed strategy is based on data acquisition via modulated wideband converters known from sub-Nyquist sampling. This paper describes how the modulators have to be modified such that signal recovery from sub-Nyquist amplitude samples becomes possible and a corresponding recovery algorithm is given which is computational efficient. In addition, the proposed strategy is fairly general, allowing for several constructions and recovery algorithms.

Includes members:
  Member "compressive phase retrieval via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "phase retrieval via structured modulations in paley wiener spaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "phase retrieval of real valued signals in a shift invariant space")
  Member "sparse signal recovery from quadratic measurements via convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "phase retrieval via matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image guided depth sampling and reconstruction")

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)