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: "probabilistic fasttext for multi sense word embeddings"
  Abstract: We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share statistical strength across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, the proposed model is the first to achieve multi-sense representations while having enriched semantics on rare words.

Connected through hyperedge HE-1 (induced by source paper "word sense disambiguation for 158 languages using word embeddings only"):
  with members:
    - "glossbert bert for word sense disambiguation with gloss knowledge"
    - "multi sense embeddings from topic models"
    - "watset local global graph clustering with applications in sense and frame induction"
    - "an unsupervised word sense disambiguation system for under resourced languages"
    - "joint multilingual supervision for cross lingual entity linking"
    - "russian word sense induction by clustering averaged word embeddings"
    - "autosense model for word sense induction"
    - "word sense induction with neural bilm and symmetric patterns"

Connected through hyperedge HE-2 (induced by source paper "bertscore evaluating text generation with bert"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "fine tune bert for extractive summarization"
    - "alternative weighting schemes for elmo embeddings"
    - "deep reinforcement learning with distributional semantic rewards for abstractive summarization"
    - "pay less attention with lightweight and dynamic convolutions"
    - "simple applications of bert for ad hoc document retrieval"
    - "unifying human and statistical evaluation for natural language generation"
    - "scibert a pretrained language model for scientific text"

Connected through hyperedge HE-3 (induced by source paper "anchor transform learning sparse representations of discrete objects"):
  with members:
    - "neural input search for large scale recommendation models"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "word embedding based on low rank doubly stochastic matrix decomposition"
    - "bayesian compression for natural language processing"
    - "adaptive input representations for neural language modeling"
    - "towards learning sparsely used dictionaries with arbitrary supports"
    - "attention is all you need"
    - "learning k way d dimensional discrete code for compact embedding representations"

Connected through hyperedge HE-4 (induced by source paper "knowledge infused learning k il towards deep incorporation of knowledge in deep learning"):
  with members:
    - "infusing domain knowledge in ai based black box models for better explainability with application in bankruptcy prediction"
    - "ernie enhanced representation through knowledge integration"
    - "predictive analysis on twitter techniques and applications"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"
    - "k bert enabling language representation with knowledge graph"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "ai reasoning systems pac and applied methods"

Connected through hyperedge HE-5 (induced by source paper "improving document classification with multi sense embeddings"):
  with members:
    - "topic modeling in embedding spaces"
    - "the dynamic embedded topic model"
    - "improving topic models with latent feature word representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "concatenated p mean word embeddings as universal cross lingual sentence representations"
    - "efficient vector representation for documents through corruption"
    - "ltsg latent topical skip gram for mutually learning topic model and vector representations"

Connected through hyperedge HE-6 (induced by source paper "sequence to set semantic tagging end to end multi label prediction using neural attention for complex query reformulation and automated text categorization"):
  with members:
    - "attention is all you need"
    - "neural summarization by extracting sentences and words"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"
    - "gated feedback recurrent neural networks"
    - "reasoning about entailment with neural attention"
    - "attention based models for speech recognition"

Connected through hyperedge HE-7 (induced by source paper "modeling islamist extremist communications on social media using contextual dimensions religion ideology and hate"):
  with members:
    - "predictive analysis on twitter techniques and applications"
    - "the rise of jihadist propaganda on social networks"
    - "domain adapted word embeddings for improved sentiment classification"
    - "what s ur type contextualized classification of user types in marijuana related communications using compositional multiview embedding"
    - "automated hate speech detection and the problem of offensive language"
    - "predicting online extremism content adopters and interaction reciprocity"
    - "enriching word vectors with subword information"
    - "knowledge will propel machine understanding of content extrapolating from current examples"

Connected through hyperedge HE-8 (induced by source paper "second order co occurrence sensitivity of skip gram with negative sampling"):
  with members:
    - "deep contextualized word representations"
    - "second order word embeddings from nearest neighbor topological features"
    - "node2vec scalable feature learning for networks"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "choosing the word most typical in context using a lexical co occurrence network"

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)