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 "exploiting rich syntactic information for semantic parsing with graph to sequence model"):
  Abstract: Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a sequential LSTM, to extract word order features while neglecting other valuable syntactic information such as dependency graph or constituent trees. In this paper, we first propose to use the \textit{syntactic graph} to represent three types of syntactic information, i.e., word order, dependency and constituency features. We further employ a graph-to-sequence model to encode the syntactic graph and decode a logical form. Experimental results on benchmark datasets show that our model is comparable to the state-of-the-art on Jobs640, ATIS and Geo880. Experimental results on adversarial examples demonstrate the robustness of the model is also improved by encoding more syntactic information.

Includes members:
  Member "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness verification for transformers")
  Member "graph2seq graph to sequence learning with attention based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards graph representation learning in emergent communication")
  Member "word mover s embedding from word2vec to document embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network")
  Member "sentence state lstm for text representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gret global representation enhanced transformer")
  Member "universal semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "synthetic and natural noise both break neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "abstract syntax networks for code generation and semantic parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "schema2qa answering complex queries on the structured web with a neural model")
  Member "cross domain semantic parsing via paraphrasing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

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)