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: "retrofitting word vectors to semantic lexicons"
  Abstract: Vector space word representations are learned from distributional information of words in large corpora. Although such statistics are semantically informative, they disregard the valuable information that is contained in semantic lexicons such as WordNet, FrameNet, and the Paraphrase Database. This paper proposes a method for refining vector space representations using relational information from semantic lexicons by encouraging linked words to have similar vector representations, and it makes no assumptions about how the input vectors were constructed. Evaluated on a battery of standard lexical semantic evaluation tasks in several languages, we obtain substantial improvements starting with a variety of word vector models. Our refinement method outperforms prior techniques for incorporating semantic lexicons into the word vector training algorithms.

Connected through hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity"):
  with members:
    - "multilingual alignment of contextual word representations"
    - "emerging cross lingual structure in pretrained language models"
    - "an empirical study on post processing methods for word embeddings"
    - "informing unsupervised pretraining with external linguistic knowledge"
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "word similarity datasets for thai construction and evaluation"
    - "paws paraphrase adversaries from word scrambling"
    - "cross lingual ability of multilingual bert an empirical study"

Connected through hyperedge HE-2 (induced by source paper "learning from explanations with neural execution tree"):
  with members:
    - "hierarchical decision making by generating and following natural language instructions"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "generalize symbolic knowledge with neural rule engine"
    - "e snli natural language inference with natural language explanations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "heterogeneous supervision for relation extraction a representation learning approach"

Connected through hyperedge HE-3 (induced by source paper "neural graph embedding methods for natural language processing"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "confidence based graph convolutional networks for semi supervised learning"
    - "hypernetwork knowledge graph embeddings"
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "lovasz convolutional networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph to sequence learning using gated graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "word embedding algorithms as generalized low rank models and their canonical form"):
  with members:
    - "attention please a critical review of neural attention models in natural language processing"
    - "how large a vocabulary does text classification need a variational approach to vocabulary selection"
    - "deep learning for sentiment analysis a survey"
    - "code2vec learning distributed representations of code"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "morphosyntactic tagging with a meta bilstm model over context sensitive token encodings"
    - "generalizing word embeddings using bag of subwords"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-5 (induced by source paper "representation learning for words and entities"):
  with members:
    - "deep multi view learning to rank"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "deep sets"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation learning of entities and documents from knowledge base descriptions"
    - "new embedded representations and evaluation protocols for inferring transitive relations"
    - "neural collaborative filtering"

Connected through hyperedge HE-6 (induced by source paper "language features matter effective language representations for vision language tasks"):
  with members:
    - "bilinear attention networks"
    - "stacked cross attention for image text matching"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "deep contextualized word representations"
    - "multilevel language and vision integration for text to clip retrieval"
    - "open vocabulary phrase detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-7 (induced by source paper "linspector multilingual probing tasks for word representations"):
  with members:
    - "linspector web a multilingual probing suite for word representations"
    - "a survey of cross lingual word embedding models"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "xnli evaluating cross lingual sentence representations"
    - "on the complexity and typology of inflectional morphological systems"
    - "allennlp a deep semantic natural language processing platform"
    - "unimorph 2 0 universal morphology"

Connected through hyperedge HE-8 (induced by source paper "knowledge enhanced attention for robust natural language inference"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "improving question answering by commonsense based pre training"
    - "annotation artifacts in natural language inference data"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "fine tuned language models for text classification"

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)