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: "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
  Abstract: We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets.

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 "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"
    - "nine features in a random forest to learn taxonomical semantic relations"
    - "node2vec scalable feature learning for networks"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "line large scale information network embedding"

Connected through hyperedge HE-3 (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-4 (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-5 (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"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "order embeddings of images and language"

Connected through hyperedge HE-6 (induced by source paper "tifi taxonomy induction for fictional domains extended version"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "taxonomy induction using hypernym subsequences"
    - "augur mining human behaviors from fiction to power interactive systems"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "inferring interpersonal relations in narrative summaries"
    - "you re mr lebowski i m the dude inducing address term formality in signed social networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "hypernym detection using strict partial order networks"):
  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"
    - "improved representation learning for predicting commonsense ontologies"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "websets extracting sets of entities from the web using unsupervised information extraction"

Connected through hyperedge HE-8 (induced by source paper "improving cross lingual word embeddings by meeting in the middle"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "robust cross lingual hypernymy detection using dependency context"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "unsupervised machine translation using monolingual corpora only"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "word translation without parallel data"

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)