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 "matching natural language sentences with hierarchical sentence factorization"):
  Abstract: Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Prior research has proposed both unsupervised distance-based schemes, when training data is not available, and deep learning schemes for sentence matching, given training data. However, previous approaches either omit or fail to fully utilize the ordered, hierarchical, and flexible structures of language objects, as well as the interaction between them. In this paper, we propose \textit{Hierarchical Sentence Factorization}—a technique that is able to factorize a sentence into a hierarchical representation, with the components at each different scale reordered into a “predicate-argument” form. The proposed sentence factorization technique leads to the invention of: 1) a new unsupervised distance metric which calculates the semantic distance between a pair of text snippets by solving a penalized optimal transport problem while preserving the logical relationship of words in the reordered sentences, and 2) new multi-scale deep learning models for supervised semantic training, based on factorized sentence hierarchies. We apply our techniques to text-pair similarity estimation and text-pair relationship classification tasks, based on multiple datasets such as STSbenchmark, the Microsoft Research paraphrase identification (MSRP) dataset, the SICK dataset, etc. Extensive experiments show that the proposed hierarchical sentence factorization can be used to significantly improve the performance of existing unsupervised distance-based metrics as well as multiple supervised deep learning models based on the convolutional neural network (CNN) and long short-term memory (LSTM).

Includes members:
  Member "semeval 2017 task 1 semantic textual similarity multilingual and crosslingual focused evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "bilateral multi perspective matching for natural language sentences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text based question answering from information retrieval and deep neural network perspectives a survey")
  Member "an incremental parser for abstract meaning representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "amr similarity metrics from principles")
  Member "sentence pair scoring towards unified framework for text comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "training neural response selection for task oriented dialogue systems")
  Member "bag of tricks for efficient text classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  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 "creation and evaluation of datasets for distributional semantics tasks in the digital humanities domain")
  Member "text matching as image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "a compare aggregate model for matching text sequences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to respond with stickers a framework of unifying multi modality in multi turn dialog")

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)