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: "on prediction using variable order markov models"
  Abstract: This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.

Connected through hyperedge HE-1 (induced by source paper "learning to structure long term dependence for sequential recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "bridging the gap between training and inference for neural machine translation"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "quantifying long range dependence in language and user behavior to improve rnns"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "variational conditional dependence hidden markov models for human action recognition"):
  with members:
    - "nonparametric bayesian deep networks with local competition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "a generative restricted boltzmann machine based method for high dimensional motion data modeling"

Connected through hyperedge HE-3 (induced by source paper "modeling event propagation via graph biased temporal point process"):
  with members:
    - "decoupled learning for factorial marked temporal point processes"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "line large scale information network embedding"
    - "question answering with subgraph embeddings"

Connected through hyperedge HE-4 (induced by source paper "uncertainty on asynchronous time event prediction"):
  with members:
    - "time dependent representation for neural event sequence prediction"
    - "bayesian recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "quantized densely connected u nets for efficient landmark localization"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "training and inference with integers in deep neural networks"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deeprebirth accelerating deep neural network execution on mobile devices"
    - "learning feature pyramids for human pose estimation"
    - "memory efficient implementation of densenets"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-6 (induced by source paper "sequence aware recommender systems"):
  with members:
    - "personalizing session based recommendations with hierarchical recurrent neural networks"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "personalized course sequence recommendations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "session based recommendations with recurrent neural networks"
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "learning distributed representations from reviews for collaborative filtering"

Connected through hyperedge HE-7 (induced by source paper "context tree for adaptive session based recommendation"):
  with members:
    - "evaluation of session based recommendation algorithms"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "recurrent latent variable networks for session based recommendation"
    - "fusing similarity models with markov chains for sparse sequential recommendation"
    - "improved recurrent neural networks for session based recommendations"
    - "session based recommendations with recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-8 (induced by source paper "joint denoising compression of image contours via shape prior and context tree"):
  with members:
    - "context tree based image contour coding using a geometric prior"
    - "merge frame design for video stream switching using piecewise constant functions"
    - "optimal lagrange multipliers for dependent rate allocation in video coding"

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)