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: "nonparametric spherical topic modeling with word embeddings"
  Abstract: Traditional topic models do not account for semantic regularities in language. Recent distributional representations of words exhibit semantic consistency over directional metrics such as cosine similarity. However, neither categorical nor Gaussian observational distributions used in existing topic models are appropriate to leverage such correlations. In this paper, we propose to use the von Mises-Fisher distribution to model the density of words over a unit sphere. Such a representation is well-suited for directional data. We use a Hierarchical Dirichlet Process for our base topic model and propose an efficient inference algorithm based on Stochastic Variational Inference. This model enables us to naturally exploit the semantic structures of word embeddings while flexibly discovering the number of topics. Experiments demonstrate that our method outperforms competitive approaches in terms of topic coherence on two different text corpora while offering efficient inference.

Connected through hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint embedding of words and labels for text classification"

Connected through hyperedge HE-2 (induced by source paper "cate category name guidedword embedding"):
  with members:
    - "guiding corpus based set expansion by auxiliary sets generation and co expansion"
    - "spherical text embedding"
    - "topic modeling in embedding spaces"
    - "higitclass keyword driven hierarchical classification of github repositories"
    - "improving topic models with latent feature word representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"

Connected through hyperedge HE-3 (induced by source paper "spherical text embedding"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "embedding text in hyperbolic spaces"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "weakly supervised hierarchical text classification"
    - "weakly supervised neural text classification"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-4 (induced by source paper "mixed curvature variational autoencoders"):
  with members:
    - "constant curvature graph convolutional networks"
    - "riemannian adaptive optimization methods"
    - "neural autoregressive flows"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hyperbolic neural networks"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "etnlp a visual aided systematic approach to select pre trained embeddings for a downstream task"):
  with members:
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "improving topic models with latent feature word representations"
    - "introducing two vietnamese datasets for evaluating semantic models of dis similarity and relatedness"
    - "vncorenlp a vietnamese natural language processing toolkit"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attentive neural network for named entity recognition in vietnamese"
    - "a fast and accurate vietnamese word segmenter"

Connected through hyperedge HE-6 (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"
    - "jointly learning word embeddings and latent topics"
    - "understanding state preferences with text as data introducing the un general debate corpus"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "jointly learning word embeddings and latent topics"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "neural variational inference for text processing"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-8 (induced by source paper "modelling semantic categories using conceptual neighborhood"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "learning conceptual space representations of interrelated concepts"
    - "embedding words and senses together via joint knowledge enhanced training"
    - "joint embedding of hierarchical categories and entities for concept categorization and dataless classification"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"

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)