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 "parametric bilinear generalized approximate message passing"):
  Abstract: We propose a scheme to estimate the parameters   $b_i$   and   $c_j$   of the bilinear form   $z_m=\sum_{i,j} b_i z_m^{(i,j) } c_j$   from noisy measurements   $\{y_m\}_{m=1}^M$  , where   $y_m$   and   $z_m$   are related through an arbitrary likelihood function and   $z_m^{(i,j) }$   are known. Our scheme is based on generalized approximate message passing (G-AMP): it treats   $b_i$   and   $c_j$   as random variables and   $z_m^{(i,j) }$   as an i.i.d. Gaussian 3-way tensor in order to derive a tractable simplification of the sum-product algorithm in the large-system limit. It generalizes previous instances of bilinear G-AMP, such as those that estimate matrices   ${B}$   and   ${C}$   from a noisy measurement of   ${Z}={BC}$  , allowing the application of AMP methods to problems such as self-calibration, blind deconvolution, and matrix compressive sensing. Numerical experiments confirm the accuracy and computational efficiency of the proposed approach.

Includes members:
  Member "phase transitions and sample complexity in bayes optimal matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "self calibration and biconvex compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")
  Member "adaptive damping and mean removal for the generalized approximate message passing algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "compressive phase retrieval via generalized approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "identifiability in blind deconvolution under minimal assumptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture")
  Member "identifiability and stability in blind deconvolution under minimal assumptions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blind phaseless short time fourier transform recovery")
  Member "identifiability in blind deconvolution with subspace or sparsity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi target detection with application to cryo electron microscopy")
  Member "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blind demixing and deconvolution at near optimal rate")

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)