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: For a learning task, data can usually be collected from different sources or be represented from multiple views. For example, laboratory results from different medical examinations are available for disease diagnosis, and each of them can only reflect the health state of a person from a particular aspect/view. Therefore, different views provide complementary information for learning tasks. An effective integration of the multi-view information is expected to facilitate the learning performance. In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively include all the possible interactions between features from multiple views. A joint factorization is embedded for the full-order interaction parameters which allows parameter estimation under sparsity. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. A stochastic gradient descent method is presented to learn the MVM model. We further illustrate the advantages of MVMs through comparison with other methods for multi-view classification, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs).

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"
    - "multi view machines"

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"
    - "multi view machines"
    - "product based neural networks for user response prediction"

Connected through hyperedge HE-3 (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-4 (induced by source paper "attribute aware collaborative filtering survey and classification"):
  with members:
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "neural factorization machines for sparse predictive analytics"
    - "neural collaborative filtering"
    - "on sampling strategies for neural network based collaborative filtering"
    - "deepfm a factorization machine based neural network for ctr prediction"
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "vbpr visual bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-5 (induced by source paper "reconciled polynomial machine a unified representation of shallow and deep learning models"):
  with members:
    - "the power of deeper networks for expressing natural functions"

Connected through hyperedge HE-6 (induced by source paper "deepmood modeling mobile phone typing dynamics for mood detection"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mining brain networks using multiple side views for neurological disorder identification"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "speech recognition with deep recurrent neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "a review of heterogeneous data mining for brain disorders"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "mining brain networks using multiple side views for neurological disorder identification"
    - "dusk a dual structure preserving kernel for supervised tensor learning with applications to neuroimages"
    - "a survey on multi view learning"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "discriminative feature selection for uncertain graph classification"

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)