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: "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
  Abstract: The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user’s musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.

Connected through hyperedge HE-1 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "multiplicative lstm for sequence modelling"
    - "tying word vectors and word classifiers a loss framework for language modeling"

Connected through hyperedge HE-2 (induced by source paper "representation exploration and recommendation of music playlists"):
  with members:
    - "deep content user embedding model for music recommendation"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "towards playlist generation algorithms using rnns trained on within track transitions"
    - "variational neural machine translation"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-3 (induced by source paper "distributed vector representations of folksong motifs"):
  with members:
    - "modeling musical context with word2vec"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "from frequency to meaning vector space models of semantics"

Connected through hyperedge HE-4 (induced by source paper "deep neural network marketplace recommenders in online experiments"):
  with members:
    - "five lessons from building a deep neural network recommender"
    - "atrank an attention based user behavior modeling framework for recommendation"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "meta prod2vec product embeddings using side information for recommendation"
    - "item2vec neural item embedding for collaborative filtering"
    - "metadata embeddings for user and item cold start recommendations"
    - "e commerce in your inbox product recommendations at scale"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "attention based mixture density recurrent networks for history based recommendation"):
  with members:
    - "related pins at pinterest the evolution of a real world recommender system"
    - "improved recurrent neural networks for session based recommendations"
    - "enriching word vectors with subword information"
    - "session based recommendations with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

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.IR(Information Retrieval)