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: "from word embeddings to item recommendation"
  Abstract: Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techniques, such as neighborhood based methods, machine-learning based methods and matrix-factorization based methods. In this work, a set of well known methods from natural language processing domain, namely Word2Vec, is applied to recommendation systems domain. Unlike previous works that use Word2Vec for recommendation, this work uses non-textual features, the check-ins, and it recommends venues to visit/check-in to the target users. For the experiments, a Foursquare check-in dataset is used. The results show that use of continuous vector space representations of items modeled by techniques of Word2Vec is promising for making recommendations.

Connected through hyperedge HE-1 (induced by source paper "a survey on session based recommender systems"):
  with members:
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "session aware information embedding for e commerce product recommendation"
    - "leveraging long and short term information in content aware movie recommendation"
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "neural attentive session based recommendation"
    - "personalizing session based recommendations with hierarchical recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"):
  with members:
    - "lstm a search space odyssey"
    - "improved recurrent neural networks for session based recommendations"
    - "item2vec neural item embedding for collaborative filtering"
    - "advances in very deep convolutional neural networks for lvcsr"
    - "theano a python framework for fast computation of mathematical expressions"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "session based recommendations with recurrent neural networks"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-3 (induced by source paper "learning continuous user representations through hybrid filtering with doc2vec"):
  with members:
    - "item2vec neural item embedding for collaborative filtering"
    - "display advertising with real time bidding rtb and behavioural targeting"
    - "a flexible recommendation system for cable tv"
    - "skip thought vectors"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of sentences and documents"
    - "real time bidding benchmarking with ipinyou dataset"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-4 (induced by source paper "cross discourse and multilingual exploration of textual corpora with the dualneighbors algorithm"):
  with members:
    - "learning word vectors for 157 languages"
    - "mrnet product2vec a multi task recurrent neural network for product embeddings"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "modelling user preferences using word embeddings for context aware venue recommendation"
    - "enriching word vectors with subword information"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "sense2vec a fast and accurate method for word sense disambiguation in neural word embeddings"

Connected through hyperedge HE-5 (induced by source paper "word2vec applied to recommendation hyperparameters matter"):
  with members:
    - "specializing joint representations for the task of product recommendation"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "mllib machine learning in apache spark"
    - "e commerce in your inbox product recommendations at scale"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "direct optimization of ranking measures"

Connected through hyperedge HE-6 (induced by source paper "gemrank global entity embedding for collaborative filtering"):
  with members:
    - "reliable graph based collaborative ranking"
    - "joint deep modeling of users and items using reviews for recommendation"
    - "sibrank signed bipartite network analysis for neighbor based collaborative ranking"
    - "e commerce in your inbox product recommendations at scale"

Connected through hyperedge HE-7 (induced by source paper "unsupervised spatio temporal embeddings for user and location modelling"):
  with members:
    - "a structured self attentive sentence embedding"
    - "word embeddings to enhance twitter gang member profile identification"
    - "semantic place descriptors for classification and map discovery"
    - "gt seer geo temporal sequential embedding rank for point of interest recommendation"
    - "deep residual learning for image recognition"
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "deepwalk online learning of social representations"
    - "exploiting similarities among languages for machine translation"

Connected through hyperedge HE-8 (induced by source paper "provable benefits of representation learning"):
  with members:
    - "provable algorithms for inference in topic models"
    - "a non generative framework and convex relaxations for unsupervised learning"
    - "a latent variable model approach to pmi based word embeddings"
    - "distributed representations of words and phrases and their compositionality"
    - "a spectral algorithm for latent dirichlet allocation"

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)