human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "finite sample analysis of approximate message passing algorithms"
  Abstract: Approximate message passing (AMP) refers to a class of efficient algorithms for statistical estimation in high-dimensional problems such as compressed sensing and low-rank matrix estimation. This paper analyzes the performance of AMP in the regime where the problem dimension is large but finite. For concreteness, we consider the setting of high-dimensional regression, where the goal is to estimate a high-dimensional vector    $\beta _{0}$    from a noisy measurement    $y=A \beta _{0} + w$   . AMP is a low-complexity, scalable algorithm for this problem. Under suitable assumptions on the measurement matrix    $A$   , AMP has the attractive feature that its performance can be accurately characterized in the large system limit by a simple scalar iteration called state evolution. Previous proofs of the validity of state evolution have all been asymptotic convergence results. In this paper, we derive a concentration inequality for AMP with Gaussian matrices with independent and identically distributed (i.i.d.) entries and finite dimension    $n \times N$   . The result shows that the probability of deviation from the state evolution prediction falls exponentially in    $n$   . This provides theoretical support for empirical findings that have demonstrated excellent agreement of AMP performance with state evolution predictions for moderately large dimensions. The concentration inequality also indicates that the number of AMP iterations    $t$    can grow no faster than order    $({\log n}/{\log \log n})$    for the performance to be close to the state evolution predictions with high probability. The analysis can be extended to obtain similar non-asymptotic results for AMP in other settings such as low-rank matrix estimation.

Connected through hyperedge HE-1 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding"):
  with members:
    - "sparse regression codes"
    - "the error probability of sparse superposition codes with approximate message passing decoding"
    - "spatially coupled sparse regression codes design and state evolution analysis"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "inference with deep generative priors in high dimensions"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "inverting deep generative models one layer at a time"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "sunlayer stable denoising with generative networks"
    - "correction by projection denoising images with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "an approximate message passing framework for side information"):
  with members:
    - "an analysis of state evolution for approximate message passing with side information"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "state evolution for approximate message passing with non separable functions"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "optimal trade offs in multi processor approximate message passing"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"

Connected through hyperedge HE-4 (induced by source paper "the error probability of sparse superposition codes with approximate message passing decoding"):
  with members:
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "phase transitions in sparse pca"
    - "information theoretically optimal sparse pca"
    - "replica analysis and approximate message passing decoder for superposition codes"

Connected through hyperedge HE-5 (induced by source paper "analysis of approximate message passing with non separable denoisers and markov random field priors"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "state evolution for approximate message passing with non separable functions"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "compressive hyperspectral imaging via approximate message passing"
    - "compressive imaging via approximate message passing with image denoising"
    - "from denoising to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-6 (induced by source paper "0 1 phase transitions in sparse spiked matrix estimation"):
  with members:
    - "mutual information for low rank even order symmetric tensor factorization"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "sum of squares lower bounds for sparse pca"
    - "information theoretically optimal sparse pca"

Connected through hyperedge HE-7 (induced by source paper "an analysis of state evolution for approximate message passing with side information"):
  with members:
    - "an approximate message passing framework for side information"
    - "state evolution for approximate message passing with non separable functions"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "message passing algorithms for synchronization problems over compact groups"):
  with members:
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "non unique games over compact groups and orientation estimation in cryo em"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "information theoretically optimal sparse pca"

Question: Please tell me which class the center node 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)