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 "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain"):
  Abstract: Word embeddings are already well studied in the general domain, usually trained on large text corpora, and have been evaluated for example on word similarity and analogy tasks, but also as an input to downstream NLP processes. In contrast, in this work we explore the suitability of word embedding technologies in the specialized digital humanities domain. After training embedding models of various types on two popular fantasy novel book series, we evaluate their performance on two task types: term analogies, and word intrusion. To this end, we manually construct test datasets with domain experts. Among the contributions are the evaluation of various word embedding techniques on the different task types, with the findings that even embeddings trained on small corpora perform well for example on the word intrusion task. Furthermore, we provide extensive and high-quality datasets in digital humanities for further investigation, as well as the implementation to easily reproduce or extend the experiments.

Includes members:
  Member "redefining context windows for word embedding models an experimental study":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "syntactic interchangeability in word embedding models")
  Member "high risk learning acquiring new word vectors from tiny data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to learn words from visual scenes")
  Member "the effects of data size and frequency range on distributional semantic models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "word class embeddings for multiclass text classification")
  Member "enriching word vectors with subword information":
    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 "problems with evaluation of word embeddings using word similarity tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "enhancing the lexvec distributed word representation model using positional contexts and external memory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relation extraction datasets in the digital humanities domain and their evaluation with word embeddings")
  Member "issues in evaluating semantic spaces using word analogies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "mining and modeling character networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "temporal embeddings and transformer models for narrative text understanding")

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)