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: "analogies explained towards understanding word embeddings"
  Abstract: Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to achieve it. Several explanations have been proposed, but each introduces assumptions that do not hold in practice. We derive a probabilistically grounded definition of paraphrasing that we re-interpret as word transformation, a mathematical description of "$w_x$ is to $w_y$". From these concepts we prove existence of linear relationships between W2V-type embeddings that underlie the analogical phenomenon, identifying explicit error terms.

Connected through hyperedge HE-1 (induced by source paper "product knowledge graph embedding for e commerce"):
  with members:
    - "a multi task learning approach for improving product title compression with user search log data"
    - "ripplenet propagating user preferences on the knowledge graph for recommender systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "multi task learning as multi objective optimization"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "attention is all you need"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-2 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "low rank approximation of matrices for pmi based word embeddings"
    - "poincar e glove hyperbolic word embeddings"
    - "towards understanding linear word analogies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what the vec towards probabilistically grounded embeddings"
    - "learned in translation contextualized word vectors"

Connected through hyperedge HE-3 (induced by source paper "language independent tokenisation rivals language specific tokenisation for word similarity prediction"):
  with members:
    - "word similarity datasets for thai construction and evaluation"
    - "a systematic study of leveraging subword information for learning word representations"
    - "how multilingual is multilingual bert"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "generalizing word embeddings using bag of subwords"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "adapting word embeddings to new languages with morphological and phonological subword representations"
    - "comparing cnn and lstm character level embeddings in bilstm crf models for chemical and disease named entity recognition"

Connected through hyperedge HE-4 (induced by source paper "guiding corpus based set expansion by auxiliary sets generation and co expansion"):
  with members:
    - "discriminative topic mining via category name guided text embedding"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "fuse multi faceted set expansion by coherent clustering of skip grams"
    - "term set expansion based nlp architect by intel ai lab"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "inducing relational knowledge from bert"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "relational word embeddings"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "comet commonsense transformers for automatic knowledge graph construction"
    - "fair is better than sensational man is to doctor as woman is to doctor"
    - "do neural language representations learn physical commonsense"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-6 (induced by source paper "multi relational poincar e graph embeddings"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "riemannian adaptive optimization methods"
    - "hyperbolic neural networks"
    - "m walk learning to walk over graphs using monte carlo tree search"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"

Connected through hyperedge HE-7 (induced by source paper "word embedding visualization via dictionary learning"):
  with members:
    - "towards understanding linear word analogies"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "spine sparse interpretable neural embeddings"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"
    - "towards universal paraphrastic sentence embeddings"

Connected through hyperedge HE-8 (induced by source paper "on understanding knowledge graph representation"):
  with members:
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "convolutional 2d knowledge graph embeddings"
    - "adam a method for stochastic optimization"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"

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)