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 "marrying up regular expressions with neural networks a case study for spoken language understanding"):
  Abstract: The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.

Includes members:
  Member "symbolic priors for rnn based semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grammatical sequence prediction for real time neural semantic parsing")
  Member "knowledge graph embedding with iterative guidance from soft rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "knowledge graphs")
  Member "learning to remember rare events":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to compare relation semantic alignment for few shot learning")
  Member "harnessing deep neural networks with logic rules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "disturblabel regularizing cnn on the loss layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "being bayesian about categorical probability")
  Member "lifted rule injection for relation embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "knowledge graphs")
  Member "matching networks for one shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "neural generation of regular expressions from natural language with minimal domain knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing")

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)