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: "hearst patterns revisited automatic hypernym detection from large text corpora"
  Abstract: Methods for unsupervised hypernym detection may broadly be categorized according to two paradigms: pattern-based and distributional methods. In this paper, we study the performance of both approaches on several hypernymy tasks and find that simple pattern-based methods consistently outperform distributional methods on common benchmark datasets. Our results show that pattern-based models provide important contextual constraints which are not yet captured in distributional methods.

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 "taxoexpan self supervised taxonomy expansion with position enhanced graph neural network"):
  with members:
    - "a user centered concept mining system for query and document understanding at tencent"
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "position aware graph neural networks"
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "hiexpan task guided taxonomy construction by hierarchical tree expansion"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-3 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing"):
  with members:
    - "syntax enhanced neural machine translation with syntax aware word representations"
    - "75 languages 1 model parsing universal dependencies universally"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semi supervised sequence modeling with cross view training"
    - "constituency parsing with a self attentive encoder"
    - "deep biaffine attention for neural dependency parsing"
    - "globally normalized transition based neural networks"

Connected through hyperedge HE-4 (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-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"
    - "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"
    - "building an evaluation scale using item response theory"

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"
    - "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"
    - "distributional inclusion vector embedding for unsupervised hypernymy detection"

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"
    - "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"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"
    - "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"
    - "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"
    - "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"

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)