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: "jointly learning word embeddings and latent topics"
  Abstract: Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a more global view, looking at the word distributions across the corpus to assign a topic to each word occurrence. These two paradigms are complementary in how they represent the meaning of word occurrences. While some previous works have already looked at using word embeddings for improving the quality of latent topics, and conversely, at using latent topics for improving word embeddings, such "two-step'' methods cannot capture the mutual interaction between the two paradigms. In this paper, we propose STE, a framework which can learn word embeddings and latent topics in a unified manner. STE naturally obtains topic-specific word embeddings, and thus addresses the issue of polysemy. At the same time, it also learns the term distributions of the topics, and the topic distributions of the documents. Our experimental results demonstrate that the STE model can indeed generate useful topic-specific word embeddings and coherent latent topics in an effective and efficient way.

Connected through hyperedge HE-1 (induced by source paper "conversational structure aware and context sensitive topic model for online discussions"):
  with members:
    - "context aware image annotation in active learning"
    - "eliminating search intent bias in learning to rank"
    - "knowledge guided text structuring in clinical trials"
    - "improving topic models with latent feature word representations"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "event detection tracking and visualization in twitter a mention anomaly based approach"
    - "probabilistic latent semantic analysis"

Connected through hyperedge HE-2 (induced by source paper "what you say and how you say it joint modeling of topics and discourse in microblog conversations"):
  with members:
    - "improving topic models with latent feature word representations"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "find the conversation killers a predictive study of thread ending posts"
    - "discovering discrete latent topics with neural variational inference"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "the dynamic embedded topic model"):
  with members:
    - "topic modeling in embedding spaces"
    - "improving topic models with latent feature word representations"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "metalda a topic model that efficiently incorporates meta information"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "nonparametric spherical topic modeling with word embeddings"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "topic modeling in embedding spaces"):
  with members:
    - "improving topic models with latent feature word representations"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "metalda a topic model that efficiently incorporates meta information"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "nonparametric spherical topic modeling with word embeddings"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-5 (induced by source paper "imparting interpretability to word embeddings while preserving semantic structure"):
  with members:
    - "analytical methods for interpretable ultradense word embeddings"
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "semantic structure and interpretability of word embeddings"
    - "adversarial propagation and zero shot cross lingual transfer of word vector specialization"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "spine sparse interpretable neural embeddings"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"

Connected through hyperedge HE-6 (induced by source paper "classinet predicting missing features for short text classification"):
  with members:
    - "sense embedding learning for word sense induction"
    - "learning distributed representations of sentences from unlabelled data"
    - "skip thought vectors"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "line large scale information network embedding"
    - "learning to understand phrases by embedding the dictionary"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"
    - "do multi sense embeddings improve natural language understanding"

Connected through hyperedge HE-7 (induced by source paper "distilled wasserstein learning for word embedding and topic modeling"):
  with members:
    - "explainable prediction of medical codes from clinical text"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "an empirical evaluation of deep learning for icd 9 code assignment using mimic iii clinical notes"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "progressive neural networks"
    - "topic compositional neural language model"
    - "multi label classification of patient notes a case study on icd code assignment"
    - "regularizing neural networks by penalizing confident output distributions"

Connected through hyperedge HE-8 (induced by source paper "a joint model of conversational discourse and latent topics on microblogs"):
  with members:
    - "improving topic models with latent feature word representations"
    - "joint modeling of content and discourse relations in dialogues"
    - "tweet2vec character based distributed representations for social media"
    - "learning reporting dynamics during breaking news for rumour detection in social media"
    - "skip thought vectors"
    - "unsupervised cross domain word representation learning"
    - "distributed representations of sentences and documents"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"

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)