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 "domain and function a dual space model of semantic relations and compositions"):
  Abstract: Given appropriate representations of the semantic relations between carpenter and wood and between mason and stone (for example, vectors in a vector space model), a suitable algorithm should be able to recognize that these relations are highly similar (carpenter is to wood as mason is to stone; the relations are analogous). Likewise, with representations of dog, house, and kennel, an algorithm should be able to recognize that the semantic composition of dog and house, dog house, is highly similar to kennel (dog house and kennel are synonymous). It seems that these two tasks, recognizing relations and compositions, are closely connected. However, up to now, the best models for relations are significantly different from the best models for compositions. In this paper, we introduce a dual-space model that unifies these two tasks. This model matches the performance of the best previous models for relations and compositions. The dual-space model consists of a space for measuring domain similarity and a space for measuring function similarity. Carpenter and wood share the same domain, the domain of carpentry. Mason and stone share the same domain, the domain of masonry. Carpenter and mason share the same function, the function of artisans. Wood and stone share the same function, the function of materials. In the composition dog house, kennel has some domain overlap with both dog and house (the domains of pets and buildings). The function of kennel is similar to the function of house (the function of shelters). By combining domain and function similarities in various ways, we can model relations, compositions, and other aspects of semantics.

Includes members:
  Member "experimenting with transitive verbs in a discocat":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collaborative training of tensors for compositional distributional semantics")
  Member "from frequency to meaning vector space models of semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bagpack a general framework to represent semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analogical reasoning on chinese morphological and semantic relations")
  Member "a uniform approach to analogies synonyms antonyms and associations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "a survey of word embeddings evaluation methods")
  Member "expressing implicit semantic relations without supervision":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using the web as an implicit training set application to noun compound syntax and semantics")
  Member "similarity of semantic relations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "sentence analogies exploring linguistic relationships and regularities in sentence 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)