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: "stochastic belief propagation a low complexity alternative to the sum product algorithm"
  Abstract: The sum-product or belief propagation (BP) algorithm is a widely-used message-passing algorithm for computing marginal distributions in graphical models with discrete variables. At the core of the BP message updates, when applied to a graphical model with pairwise interactions, lies a matrix-vector product with complexity that is quadratic in the state dimension $d$, and requires transmission of a $(d-1)$-dimensional vector of real numbers (messages) to its neighbors. Since various applications involve very large state dimensions, such computation and communication complexities can be prohibitively complex. In this paper, we propose a low-complexity variant of BP, referred to as stochastic belief propagation (SBP). As suggested by the name, it is an adaptively randomized version of the BP message updates in which each node passes randomly chosen information to each of its neighbors. The SBP message updates reduce the computational complexity (per iteration) from quadratic to linear in $d$, without assuming any particular structure of the potentials, and also reduce the communication complexity significantly, requiring only $\log{d}$ bits transmission per edge. Moreover, we establish a number of theoretical guarantees for the performance of SBP, showing that it converges almost surely to the BP fixed point for any tree-structured graph, and for graphs with cycles satisfying a contractivity condition. In addition, for these graphical models, we provide non-asymptotic upper bounds on the convergence rate, showing that the $\ell_{\infty}$ norm of the error vector decays no slower than $O(1/\sqrt{t})$ with the number of iterations $t$ on trees and the mean square error decays as $O(1/t)$ for general graphs. These analysis show that SBP can provably yield reductions in computational and communication complexities for various classes of graphical models.

Connected through hyperedge HE-1 (induced by source paper "continuous graph flow for flexible density estimation"):
  with members:
    - "layoutvae stochastic scene layout generation from a label set"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "knowledge base completion with out of knowledge base entities a graph neural network approach"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"

Connected through hyperedge HE-2 (induced by source paper "optimization of the belief propagation algorithm for distributed detection by linear data fusion techniques"):
  with members:
    - "random interruptions in cooperation for spectrum sensing in cognitive radio networks"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "optimal multiband joint detection for spectrum sensing in cognitive radio networks"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed sensing"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-3 (induced by source paper "inference in probabilistic graphical models by graph neural networks"):
  with members:
    - "situation recognition with graph neural networks"
    - "neural message passing for quantum chemistry"
    - "convolutional pose machines"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "the more you know using knowledge graphs for image classification"
    - "discriminative embeddings of latent variable models for structured data"
    - "deeply learning the messages in message passing inference"

Connected through hyperedge HE-4 (induced by source paper "block belief propagation for parameter learning in markov random fields"):
  with members:
    - "deeply learning the messages in message passing inference"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "learning graphical model parameters with approximate marginal inference"
    - "efficient decomposed learning for structured prediction"
    - "counting belief propagation"
    - "convexifying the bethe free energy"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-5 (induced by source paper "inference based distributed channel allocation in wireless sensor networks"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-6 (induced by source paper "low complexity stochastic generalized belief propagation"):
  with members:
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "counting belief propagation"
    - "faster algorithms for max product message passing"

Connected through hyperedge HE-7 (induced by source paper "a novel stochastic decoding of ldpc codes with quantitative guarantees"):
  with members:
    - "belief propagation for continuous state spaces stochastic message passing with quantitative guarantees"
    - "relaxed half stochastic belief propagation"

Connected through hyperedge HE-8 (induced by source paper "belief propagation for continuous state spaces stochastic message passing with quantitative guarantees"):
  with members:
    - "kernel belief propagation"
    - "sufficient conditions for convergence of the sum product algorithm"

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)