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 "from frequency to meaning vector space models of semantics"):
  Abstract: Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

Includes members:
  Member "empirical analysis of predictive algorithms for collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "roget s thesaurus and semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning")
  Member "a uniform approach to analogies synonyms antonyms and associations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural latent relational analysis to capture lexical semantic relations in a vector space")
  Member "the latent relation mapping engine algorithm and experiments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of word embeddings evaluation methods")
  Member "empirical evaluation of four tensor decomposition algorithms":
    also connected through other hyperedges:
      (none)
  Member "similarity of semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "from f to a on the n y regents science exams an overview of the aristo project")
  Member "corpus based learning of analogies and semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence embeddings")
  Member "measuring semantic similarity by latent relational analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relational word embeddings")

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)