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: "residual belief propagation informed scheduling for asynchronous message passing"
  Abstract: Inference for probabilistic graphical models is still very much a practical challenge in large domains. The commonly used and effective belief propagation (BP) algorithm and its generalizations often do not converge when applied to hard, real-life inference tasks. While it is widely recognized that the scheduling of messages in these algorithms may have significant consequences, this issue remains largely unexplored. In this work, we address the question of how to schedule messages for asynchronous propagation so that a fixed point is reached faster and more often. We first show that any reasonable asynchronous BP converges to a unique fixed point under conditions similar to those that guarantee convergence of synchronous BP. In addition, we show that the convergence rate of a simple round-robin schedule is at least as good as that of synchronous propagation. We then propose residual belief propagation (RBP), a novel, easy-to-implement, asynchronous propagation algorithm that schedules messages in an informed way, that pushes down a bound on the distance from the fixed point. Finally, we demonstrate the superiority of RBP over state-of-the-art methods for a variety of challenging synthetic and real-life problems: RBP converges significantly more often than other methods; and it significantly reduces running time until convergence, even when other methods converge.

Connected through hyperedge HE-1 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "graph partition neural networks for semi supervised classification"
    - "fast generation for convolutional autoregressive models"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-2 (induced by source paper "factorized graph representations for semi supervised learning from sparse data"):
  with members:
    - "snorkel rapid training data creation with weak supervision"
    - "a general framework for estimating graphlet statistics via random walk"
    - "semi supervised classification with graph convolutional networks"
    - "graph based semi supervised learning for relational networks"
    - "semi supervised learning with heterophily"
    - "localization and centrality in networks"
    - "linearized and single pass belief propagation"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "the lock free k lsm relaxed priority queue"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-4 (induced by source paper "machine learning systems for highly distributed and rapidly growing data"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "semi cyclic stochastic gradient descent"
    - "the non iid data quagmire of decentralized machine learning"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "group normalization"
    - "federated learning with non iid data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-5 (induced by source paper "amortized bethe free energy minimization for learning mrfs"):
  with members:
    - "a tutorial on deep latent variable models of natural language"
    - "learning approximate inference networks for structured prediction"
    - "inference in probabilistic graphical models by graph neural networks"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "neural variational inference and learning in undirected graphical models"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for channel coding learned bit flipping decoding"):
  with members:
    - "learning to flip successive cancellation decoding of polar codes with lstm networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "decoding reed muller codes using minimum weight parity checks"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "an introduction to deep learning for the physical layer"
    - "soft decision decoding of reed muller codes recursive lists"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-7 (induced by source paper "message scheduling for performant many core belief propagation"):
  with members:
    - "graph partition neural networks for semi supervised classification"
    - "gunrock gpu graph analytics"
    - "loopy belief propagation for approximate inference an empirical study"
    - "sufficient conditions for convergence of the sum product algorithm"

Connected through hyperedge HE-8 (induced by source paper "coding techniques for future wi fi"):
  with members:
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"

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.AI(Artificial Intelligence)