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: "inferring concept hierarchies from text corpora via hyperbolic embeddings"
  Abstract: We consider the task of inferring is-a relationships from large text corpora. For this purpose, we propose a new method combining hyperbolic embeddings and Hearst patterns. This approach allows us to set appropriate constraints for inferring concept hierarchies from distributional contexts while also being able to predict missing is-a relationships and to correct wrong extractions. Moreover -- and in contrast with other methods -- the hierarchical nature of hyperbolic space allows us to learn highly efficient representations and to improve the taxonomic consistency of the inferred hierarchies. Experimentally, we show that our approach achieves state-of-the-art performance on several commonly-used benchmarks.

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"
    - "k bert enabling language representation with knowledge graph"

Connected through hyperedge HE-2 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"
    - "embedding text in hyperbolic spaces"

Connected through hyperedge HE-3 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"
    - "privacy preserving neural representations of text"

Connected through hyperedge HE-4 (induced by source paper "inducing hypernym relationships based on order theory"):
  with members:
    - "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"
    - "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-5 (induced by source paper "hypernym detection using strict partial order networks"):
  with members:
    - "hearst patterns revisited automatic hypernym detection from large text corpora"
    - "hierarchical density order embeddings"
    - "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"
    - "websets extracting sets of entities from the web using unsupervised information extraction"

Connected through hyperedge HE-6 (induced by source paper "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"):
  with members:
    - "hyperbolic image embeddings"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "inriasac simple hypernym extraction methods"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "on the compositionality prediction of noun phrases using poincar e embeddings"):
  with members:
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "hyperbolic image embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "non compositional term dependence for information retrieval"
    - "distributed representations of words and phrases and their compositionality"

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)