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 "statistical physics and information theory perspectives on linear inverse problems"):
  Abstract: Many real-world problems in machine learning, signal processing, and communications assume that an unknown vector $x$ is measured by a matrix A, resulting in a vector $y=Ax+z$, where $z$ denotes the noise; we call this a single measurement vector (SMV) problem. Sometimes, multiple dependent vectors $x^{(j)}, j\in \{1,...,J\}$, are measured at the same time, forming the so-called multi-measurement vector (MMV) problem. Both SMV and MMV are linear models (LM's), and the process of estimating the underlying vector(s) $x$ from an LM given the matrices, noisy measurements, and knowledge of the noise statistics, is called a linear inverse problem. In some scenarios, the matrix A is stored in a single processor and this processor also records its measurements $y$; this is called centralized LM. In other scenarios, multiple sites are measuring the same underlying unknown vector $x$, where each site only possesses part of the matrix A; we call this multi-processor LM. Recently, due to an ever-increasing amount of data and ever-growing dimensions in LM's, it has become more important to study large-scale linear inverse problems. In this dissertation, we take advantage of tools in statistical physics and information theory to advance the understanding of large-scale linear inverse problems. The intuition of the application of statistical physics to our problem is that statistical physics deals with large-scale problems, and we can make an analogy between an LM and a thermodynamic system. In terms of information theory, although it was originally developed to characterize the theoretic limits of digital communication systems, information theory was later found to be rather useful in analyzing and understanding other inference problems. (The full abstract cannot fit in due to the space limit. Please refer to the PDF.)

Includes members:
  Member "approximate message passing decoder and capacity achieving sparse superposition codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "approximate message passing algorithm with universal denoising and gaussian mixture learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors")
  Member "performance trade offs in multi processor approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an approximate message passing framework for side information")
  Member "multi processor approximate message passing using lossy compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an overview of multi processor approximate message passing")
  Member "phase transitions in sparse pca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the kikuchi hierarchy and tensor pca")
  Member "compressive imaging via approximate message passing with image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "block coordinate regularization by denoising")
  Member "improving m sbl for joint sparse recovery using a subspace penalty":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance limits for noisy multimeasurement vector problems")
  Member "mismatched estimation in large linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate message passing with universal denoising")

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)