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 "compositional distributional semantics with compact closed categories and frobenius algebras"):
  Abstract: The provision of compositionality in distributional models of meaning, where a word is represented as a vector of co-occurrence counts with every other word in the vocabulary, offers a solution to the fact that no text corpus, regardless of its size, is capable of providing reliable co-occurrence statistics for anything but very short text constituents. The purpose of a compositional distributional model is to provide a function that composes the vectors for the words within a sentence, in order to create a vectorial representation that re ects its meaning. Using the abstract mathematical framework of category theory, Coecke, Sadrzadeh and Clark showed that this function can directly depend on the grammatical structure of the sentence, providing an elegant mathematical counterpart of the formal semantics view. The framework is general and compositional but stays abstract to a large extent.   This thesis contributes to ongoing research related to the above categorical model in three ways: Firstly, I propose a concrete instantiation of the abstract framework based on Frobenius algebras (joint work with Sadrzadeh). The theory improves shortcomings of previous proposals, extends the coverage of the language, and is supported by experimental work that improves existing results. The proposed framework describes a new class of compositional models thatfind intuitive interpretations for a number of linguistic phenomena.   Secondly, I propose and evaluate in practice a new compositional methodology which explicitly deals with the different levels of lexical ambiguity (joint work with Pulman). A concrete algorithm is presented, based on the separation of vector disambiguation from composition in an explicit prior step. Extensive experimental work shows that the proposed methodology indeed results in more accurate composite representations for the framework of Coecke et al. in particular and every other class of compositional models in general.   As a last contribution, I formalize the explicit treatment of lexical ambiguity in the context of the categorical framework by resorting to categorical quantum mechanics (joint work with Coecke). In the proposed extension, the concept of a distributional vector is replaced with that of a density matrix, which compactly represents a probability distribution over the potential different meanings of the specific word. Composition takes the form of quantum measurements, leading to interesting analogies between quantum physics and linguistics.

Includes members:
  Member "the frobenius anatomy of word meanings ii possessive relative pronouns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the mathematics of text structure")
  Member "a frobenius model of information structure in categorical compositional distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentence entailment in compositional distributional semantics")
  Member "efficient non parametric estimation of multiple embeddings per word in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "autoblock a hands off blocking framework for entity matching")
  Member "open system categorical quantum semantics in natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the mathematics of text structure")
  Member "resolving lexical ambiguity in tensor regression models of meaning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "determining semantic textual similarity using natural deduction proofs")
  Member "compositional operators in distributional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the compositionality of neural networks integrating symbolism and connectionism")
  Member "a study of entanglement in a categorical framework of natural language":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the mathematics of text structure")
  Member "investigating the role of prior disambiguation in deep learning compositional models of meaning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compositional vector space models for knowledge base completion")

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)