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: "experiments with three approaches to recognizing lexical entailment"
  Abstract: Inference in natural language often involves recognizing lexical entailment (RLE); that is, identifying whether one word entails another. For example, buy entails own. Two general strategies for RLE have been proposed: One strategy is to manually construct an asymmetric similarity measure for context vectors (directional similarity) and another is to treat RLE as a problem of learning to recognize semantic relations using supervised machine learning techniques (relation classification). In this paper, we experiment with two recent state-of-the-art representatives of the two general strategies. The first approach is an asymmetric similarity measure (an instance of the directional similarity strategy), designed to capture the degree to which the contexts of a word, a, form a subset of the contexts of another word, b. The second approach (an instance of the relation classification strategy) represents a word pair, a:b, with a feature vector that is the concatenation of the context vectors of a and b, and then applies supervised learning to a training set of labeled feature vectors. Additionally, we introduce a third approach that is a new instance of the relation classification strategy. The third approach represents a word pair, a : b, with a feature vector in which the features are the differences in the similarities of a and b to a set of reference words. All three approaches use vector space models (VSMs) of semantics, based on word–context matrices. We perform an extensive evaluation of the three approaches using three different datasets. The proposed new approach (similarity differences) performs significantly better than the other two approaches on some datasets and there is no dataset for which it is significantly worse. Along the way, we address some of the concerns raised in past research, regarding the treatment of RLE as a problem of semantic relation classification, and we suggest it is beneficial to make connections between the research in lexical entailment and the research in semantic relation classification.

Connected through hyperedge HE-1 (induced by source paper "assessing the lexico semantic relational knowledge captured by word and concept embeddings"):
  with members:
    - "not just about size a study on the role of distributed word representations in the analysis of scientific publications"
    - "a decomposable attention model for natural language inference"
    - "swivel improving embeddings by noticing what s missing"
    - "enriching word vectors with subword information"
    - "path based vs distributional information in recognizing lexical semantic relations"
    - "proje embedding projection for knowledge graph completion"
    - "visualizing and understanding neural models in nlp"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "learning multi sense word distributions using approximate kullback leibler divergence"):
  with members:
    - "ukp athene multi sentence textual entailment for claim verification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "stance detection with bidirectional conditional encoding"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "learning longer memory in recurrent neural networks"
    - "word representations via gaussian embedding"

Connected through hyperedge HE-3 (induced by source paper "context mover s distance barycenters optimal transport of contexts for building representations"):
  with members:
    - "poincar e glove hyperbolic word embeddings"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "word mover s embedding from word2vec to document embedding"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-4 (induced by source paper "unsupervised hypernym detection by distributional inclusion vector embedding"):
  with members:
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "poincar e embeddings for learning hierarchical representations"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "lifted rule injection for relation embeddings"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms"

Connected through hyperedge HE-5 (induced by source paper "hyperlex a large scale evaluation of graded lexical entailment"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "morph fitting fine tuning word vector spaces with simple language specific rules"
    - "baselines and test data for cross lingual inference"
    - "bilingual learning of multi sense embeddings with discrete autoencoders"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a decomposable attention model for natural language inference"
    - "intrinsic subspace evaluation of word embedding representations"
    - "counter fitting word vectors to linguistic constraints"

Connected through hyperedge HE-6 (induced by source paper "a vector space for distributional semantics for entailment"):
  with members:
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "word representations via gaussian embedding"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "commonsense knowledge enhanced embeddings for solving pronoun disambiguation problems in winograd schema challenge"):
  with members:
    - "probabilistic reasoning via deep learning neural association models"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "the goldilocks principle reading children s books with explicit memory representations"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"):
  with members:
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"
    - "scikit learn machine learning in python"

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)