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: "hypernyms under siege linguistically motivated artillery for hypernymy detection"
  Abstract: The fundamental role of hypernymy in NLP has motivated the development of many methods for the automatic identification of this relation, most of which rely on word distribution. We investigate an extensive number of such unsupervised measures, using several distributional semantic models that differ by context type and feature weighting. We analyze the performance of the different methods based on their linguistic motivation. Comparison to the state-of-the-art supervised methods shows that while supervised methods generally outperform the unsupervised ones, the former are sensitive to the distribution of training instances, hurting their reliability. Being based on general linguistic hypotheses and independent from training data, unsupervised measures are more robust, and therefore are still useful artillery for hypernymy detection.

Connected through hyperedge HE-1 (induced by source paper "the polar framework polar opposites enable interpretability of pre trained word embeddings"):
  with members:
    - "semaxis a lightweight framework to characterize domain specific word semantics beyond sentiment"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "spine sparse interpretable neural embeddings"
    - "image captioning with semantic attention"
    - "why should i trust you explaining the predictions of any classifier"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "sparse overcomplete word vector representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"

Connected through hyperedge HE-2 (induced by source paper "using distributional thesaurus embedding for co hyponymy detection"):
  with members:
    - "network features based co hyponymy detection"
    - "can network embedding of distributional thesaurus be combined with word vectors for better representation"
    - "struc2vec learning node representations from structural identity"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (induced by source paper "discovering hypernymy in text rich heterogeneous information network by exploiting context granularity"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "scoring lexical entailment with a supervised directional similarity network"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "term definitions help hypernymy detection"

Connected through hyperedge HE-4 (induced by source paper "meemi a simple method for post processing cross lingual word embeddings"):
  with members:
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "the limitations of cross language word embeddings evaluation"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "non adversarial unsupervised word translation"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-5 (induced by source paper "sherliic a typed event focused lexical inference benchmark for evaluating natural language inference"):
  with members:
    - "annotation artifacts in natural language inference data"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "stress test evaluation for natural language inference"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "generating natural adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "inferring concept hierarchies from text corpora via hyperbolic embeddings"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "taskonomy disentangling task transfer learning"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "embedding text in hyperbolic spaces"
    - "hierarchical density order embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "why we have switched from building full fledged taxonomies to simply detecting hypernymy relations"

Connected through hyperedge HE-7 (induced by source paper "learning taxonomies of concepts and not words using contextualized word representations a position paper"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "taxonomy induction using hypernym subsequences"
    - "distributional inclusion vector embedding for unsupervised hypernymy detection"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "commonly uncommon semantic sparsity in situation recognition"

Connected through hyperedge HE-8 (induced by source paper "inducing hypernym relationships based on order theory"):
  with members:
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "hierarchical density order embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "improved representation learning for predicting commonsense ontologies"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "order embeddings of images and language"

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)