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: "hierarchical neural language models for joint representation of streaming documents and their content"
  Abstract: We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a hierarchical framework with two embedded neural language models. In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them. The models learn continuous vector representations for both word tokens and documents such that semantically similar documents and words are close in a common vector space. We discuss extensions to our model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences. We validated the learned representations on a public movie rating data set from MovieLens, as well as on a large-scale Yahoo News data comprising three months of user activity logs collected on Yahoo servers. The results indicate that the proposed model can learn useful representations of both documents and word tokens, outperforming the current state-of-the-art by a large margin.

Connected through hyperedge HE-1 (induced by source paper "cb2cf a neural multiview content to collaborative filtering model for completely cold item recommendations"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "bayesian neural word embedding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "regularizing matrix factorization with user and item embeddings for recommendation"):
  with members:
    - "neural collaborative filtering"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "item2vec neural item embedding for collaborative filtering"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "a generic coordinate descent framework for learning from implicit feedback"
    - "learning distributed representations from reviews for collaborative filtering"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "inferring complementary products from baskets and browsing sessions"):
  with members:
    - "meta prod2vec product embeddings using side information for recommendation"
    - "node2vec scalable feature learning for networks"
    - "inferring networks of substitutable and complementary products"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "bpr bayesian personalized ranking from implicit feedback"

Connected through hyperedge HE-4 (induced by source paper "network representation learning a survey"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "bernoulli embeddings for graphs"
    - "graph embedding techniques applications and performance a survey"
    - "struc2vec learning node representations from structural identity"
    - "an attention based collaboration framework for multi view network representation learning"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"

Connected through hyperedge HE-5 (induced by source paper "parvecmf a paragraph vector based matrix factorization recommender system"):
  with members:
    - "from word embeddings to item recommendation"
    - "e commerce in your inbox product recommendations at scale"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-6 (induced by source paper "keyvec key semantics preserving document representations"):
  with members:
    - "document embedding with paragraph vectors"
    - "distributed representations of sentences and documents"
    - "neural machine translation by jointly learning to align and translate"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "getting started with neural models for semantic matching in web search"):
  with members:
    - "a deep relevance matching model for ad hoc retrieval"
    - "query expansion with locally trained word embeddings"
    - "unsupervised efficient and semantic expertise retrieval"
    - "learning latent vector spaces for product search"
    - "learning distributed representations of sentences from unlabelled data"
    - "learning dynamic classes of events using stacked multilayer perceptron networks"
    - "deep learning relevance creating relevant information as opposed to retrieving it"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

Connected through hyperedge HE-8 (induced by source paper "the deep journey from content to collaborative filtering"):
  with members:
    - "neural collaborative filtering"
    - "item2vec neural item embedding for collaborative filtering"
    - "bayesian neural word embedding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "a sensitivity analysis of and practitioners guide to convolutional neural networks for sentence classification"
    - "adam a method for stochastic optimization"
    - "collaborative deep learning for recommender systems"
    - "convolutional neural networks for sentence 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.CL(Computation and Language)