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: "multi view machines"
  Abstract: With rapidly growing amount of data available on the web, it becomes increasingly likely to obtain data from different perspectives for multi-view learning. Some successive examples of web applications include recommendation and target advertising. Specifically, to predict whether a user will click an ad in a query context, there are available features extracted from user profile, ad information and query description, and each of them can only capture part of the task signals from a particular aspect/view. Different views provide complementary information to learn a practical model for these applications. Therefore, an effective integration of the multi-view information is critical to facilitate the learning performance.   In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively explore the full-order interactions between features from multiple views. A joint factorization is applied for the interaction parameters which makes parameter estimation more accurate under sparsity and renders the model with the capacity to avoid overfitting. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. The advantages of MVMs are illustrated through comparison with other methods for multi-view prediction, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs).   A stochastic gradient descent method and a distributed implementation on Spark are presented to learn the MVM model. Through empirical studies on two real-world web application datasets, we demonstrate the effectiveness of MVMs on modeling feature interactions in multi-view data. A 3.51\% accuracy improvement is shown on MVMs over FMs for the problem of movie rating prediction, and 0.57\% for ad click prediction.

Connected through hyperedge HE-1 (induced by source paper "generalized embedding machines for recommender systems"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "neural factorization machines for sparse predictive analytics"
    - "attention is all you need"
    - "deep cross network for ad click predictions"
    - "strongly hierarchical factorization machines and anova kernel regression"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "interaction aware factorization machines for recommender systems"):
  with members:
    - "progressive neural architecture search"
    - "neural factorization machines for sparse predictive analytics"
    - "deep cross network for ad click predictions"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"
    - "wide deep learning for recommender systems"
    - "product based neural networks for user response prediction"
    - "multi view machines"

Connected through hyperedge HE-3 (induced by source paper "speech driven facial animation using polynomial fusion of features"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "polygan high order polynomial generators"
    - "voxceleb a large scale speaker identification dataset"
    - "mocogan decomposing motion and content for video generation"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"
    - "generating videos with scene dynamics"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep learning towards mobile applications"):
  with members:
    - "privynet a flexible framework for privacy preserving deep neural network training"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning differentially private recurrent language models"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-5 (induced by source paper "learning from multi view multi way data via structural factorization machines"):
  with members:
    - "neural factorization machines for sparse predictive analytics"
    - "deep embedding forest forest based serving with deep embedding features"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "deep cross network for ad click predictions"
    - "a broad learning approach for context aware mobile application recommendation"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "attentional factorization machines learning the weight of feature interactions via attention networks"

Connected through hyperedge HE-6 (induced by source paper "a broad learning approach for context aware mobile application recommendation"):
  with members:
    - "online unsupervised multi view feature selection"

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.LG(Machine Learning)