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: "unsupervised hypernym detection by distributional inclusion vector embedding"
  Abstract: Modeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, such as WordNet, limit the coverage of these models, which can be addressed by learning hypernyms from unlabeled text. Existing unsupervised methods either do not scale to large vocabularies or yield unacceptably poor accuracy. This paper introduces distributional inclusion vector embedding (DIVE), a simple-to-implement unsupervised method of hypernym discovery via per-word non-negative vector embeddings learned by modeling diversity of word context with specialized negative sampling. In an experimental evaluation more comprehensive than any previous literature of which we are aware - evaluating on 11 datasets using multiple existing as well as newly proposed scoring metrics - we find that our method can provide up to double or triple the precision of previous unsupervised methods, and also sometimes outperforms previous semi-supervised methods, yielding many new state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "composition of sentence embeddings lessons from statistical relational learning"):
  with members:
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "pair2vec compositional word pair embeddings for cross sentence inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"
    - "evaluating compositionality in sentence embeddings"
    - "natural language inference over interaction space"
    - "evaluating vector space models of analogy"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "embedding text in hyperbolic spaces"):
  with members:
    - "trimming and improving skip thought vectors"
    - "hierarchical embeddings for hypernymy detection and directionality"
    - "discourse based objectives for fast unsupervised sentence representation learning"
    - "dissent sentence representation learning from explicit discourse relations"
    - "specialising word vectors for lexical entailment"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "speeding up context based sentence representation learning with non autoregressive convolutional decoding"
    - "hyperlex a large scale evaluation of graded lexical entailment"

Connected through hyperedge HE-3 (induced by source paper "new embedded representations and evaluation protocols for inferring transitive relations"):
  with members:
    - "learning distributed representations of texts and entities from knowledge base"
    - "finer grained entity typing with typenet"
    - "an interpretable knowledge transfer model for knowledge base completion"
    - "poincar e embeddings for learning hierarchical representations"
    - "corpus level fine grained entity typing using contextual information"
    - "an attentive neural architecture for fine grained entity type classification"
    - "complex embeddings for simple link prediction"
    - "entity embeddings with conceptual subspaces as a basis for plausible reasoning"

Connected through hyperedge HE-4 (induced by source paper "term definitions help hypernymy detection"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "specialising word vectors for lexical entailment"
    - "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"
    - "learning to understand phrases by embedding the dictionary"
    - "a large annotated corpus for learning natural language inference"
    - "word representations via gaussian embedding"

Connected through hyperedge HE-5 (induced by source paper "end to end reinforcement learning for automatic taxonomy induction"):
  with members:
    - "why we have switched from building full fledged taxonomies to simply detecting hypernymy relations"
    - "taxonomy induction using hypernym subsequences"
    - "lifted rule injection for relation embeddings"
    - "learning concept taxonomies from multi modal data"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "one billion word benchmark for measuring progress in statistical language modeling"

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)