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: "improving hypernymy detection with an integrated path based and distributional method"
  Abstract: Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complementary approaches. Distributional methods, whose supervised variants are the current best performers, and path-based methods, which received less research attention. We suggest an improved path-based algorithm, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods. We then extend the approach to integrate both path-based and distributional signals, significantly improving upon the state-of-the-art on this task.

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 "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 "relational word embeddings"):
  with members:
    - "integrating multiplicative features into supervised distributional methods for lexical entailment"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "filling missing paths modeling co occurrences of word pairs and dependency paths for recognizing lexical semantic relations"
    - "neural latent relational analysis to capture lexical semantic relations in a vector space"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "specialising word vectors for lexical entailment"
    - "hyperlex a large scale evaluation of graded lexical entailment"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"

Connected through hyperedge HE-5 (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-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"
    - "hypernyms under siege linguistically motivated artillery for hypernymy detection"

Connected through hyperedge HE-8 (induced by source paper "logician a unified end to end neural approach for open domain information extraction"):
  with members:
    - "neural wikipedian generating textual summaries from knowledge base triples"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "question answering and question generation as dual tasks"
    - "answering complex questions using open information extraction"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "modeling coverage for neural machine translation"
    - "end to end relation extraction using lstms on sequences and tree structures"

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)