human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "hierarchical embeddings for hypernymy detection and directionality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "specialising word vectors for lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inducing relational knowledge from bert")
  Member "poincar e embeddings for learning hierarchical representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the tensor brain semantic decoding for perception and memory")
  Member "hyperlex a large scale evaluation of graded lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keml a knowledge enriched meta learning framework for lexical relation classification")
  Member "lifted rule injection for relation embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "hypernyms under siege linguistically motivated artillery for hypernymy detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the polar framework polar opposites enable interpretability of pre trained word embeddings")
  Member "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical embeddings for hypernymy detection and directionality")

Question: Please tell me which class the source paper 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)