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 "robust semantic parsing with adversarial learning for domain generalization"):
  Abstract: This paper addresses the issue of generalization for Semantic Parsing in an adversarial framework. Building models that are more robust to inter-document variability is crucial for the integration of Semantic Parsing technologies in real applications. The underlying question throughout this study is whether adversarial learning can be used to train models on a higher level of abstraction in order to increase their robustness to lexical and stylistic variations.#R##N#We propose to perform Semantic Parsing with a domain classification adversarial task without explicit knowledge of the domain. The strategy is first evaluated on a French corpus of encyclopedic documents, annotated with FrameNet, in an information retrieval perspective, then on PropBank Semantic Role Labeling task on the CoNLL-2005 benchmark. We show that adversarial learning increases all models generalization capabilities both on in and out-of-domain data.

Includes members:
  Member "adversarial domain adaptation for duplicate question detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial domain adaptation for machine reading comprehension")
  Member "sources of complexity in semantic frame parsing for information extraction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maskparse deskin at semeval 2019 task 1 cross lingual ucca semantic parsing using recursive masked sequence tagging")
  Member "adversarial removal of demographic attributes from text data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "semantic frame parsing for information extraction the calor corpus":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maskparse deskin at semeval 2019 task 1 cross lingual ucca semantic parsing using recursive masked sequence tagging")
  Member "linguistically informed self attention for semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach")
  Member "deep semantic role labeling with self attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "normalized and geometry aware self attention network for image captioning")
  Member "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems")
  Member "adversarial multi task learning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")

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)