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: "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
  Abstract: Recent work on word embeddings has shown that simple vector subtraction over pre-trained embeddings is surprisingly effective at capturing different lexical relations, despite lacking explicit supervision. Prior work has evaluated this intriguing result using a word analogy prediction formulation and hand-selected relations, but the generality of the finding over a broader range of lexical relation types and different learning settings has not been evaluated. In this paper, we carry out such an evaluation in two learning settings: (1) spectral clustering to induce word relations, and (2) supervised learning to classify vector differences into relation types. We find that word embeddings capture a surprising amount of information, and that, under suitable supervised training, vector subtraction generalises well to a broad range of relations, including over unseen lexical items.

Connected through hyperedge HE-1 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification"):
  with members:
    - "relational word embeddings"
    - "kg bert bert for knowledge graph completion"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "ernie enhanced language representation with informative entities"
    - "albert a lite bert for self supervised learning of language representations"
    - "meta relational learning for few shot link prediction in knowledge graphs"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"

Connected through hyperedge HE-2 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "tackling climate change with machine learning"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "temporal and aspectual entailment"):
  with members:
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "using j k fold cross validation to reduce variance when tuning nlp models"
    - "one representation per word does it make sense for composition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-4 (induced by source paper "inducing relational knowledge from bert"):
  with members:
    - "analogies explained towards understanding word embeddings"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-6 (induced by source paper "costra 1 0 a dataset of complex sentence transformations"):
  with members:
    - "pitfalls in the evaluation of sentence embeddings"
    - "derivational morphological relations in word embeddings"
    - "no training required exploring random encoders for sentence classification"
    - "examining structure of word embeddings with pca"
    - "in search for linear relations in sentence embedding spaces"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-7 (induced by source paper "analyzing structures in the semantic vector space a framework for decomposing word embeddings"):
  with members:
    - "imparting interpretability to word embeddings while preserving semantic structure"
    - "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"
    - "rotations and interpretability of word embeddings the case of the russian language"
    - "recent trends in deep learning based natural language processing"
    - "intrinsic subspace evaluation of word embedding representations"
    - "linear algebraic structure of word senses with applications to polysemy"

Connected through hyperedge HE-8 (induced by source paper "evaluating the utility of document embedding vector difference for relation learning"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "capturing long range contextual dependencies with memory enhanced conditional random fields"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "learning distributed representations of sentences from unlabelled data"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"

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)