human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "semantic frame parsing for information extraction the calor corpus"
  Abstract: This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The main difference in our approach compared to previous works on semantic parsing with FrameNet is that we are not interested here in full text parsing but rather on partial parsing. The goal is to select from the FrameNet resources the minimal set of frames that are going to be useful for the applicative framework targeted, in our case Information Extraction from encyclopedic documents. Such an approach leverages the manual annotation of larger corpora than those obtained through full text parsing and therefore opens the door to alternative methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark corpus. The approaches compared in this study rely on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The models compared are CRFs and multitasks bi-LSTMs.

Connected through hyperedge HE-1 (induced by source paper "robust semantic parsing with adversarial learning for domain generalization"):
  with members:
    - "adversarial domain adaptation for duplicate question detection"
    - "sources of complexity in semantic frame parsing for information extraction"
    - "adversarial removal of demographic attributes from text data"
    - "linguistically informed self attention for semantic role labeling"
    - "deep semantic role labeling with self attention"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "adversarial multi task learning for text classification"
    - "word translation without parallel data"

Connected through hyperedge HE-2 (induced by source paper "maskparse deskin at semeval 2019 task 1 cross lingual ucca semantic parsing using recursive masked sequence tagging"):
  with members:
    - "robust semantic parsing with adversarial learning for domain generalization"
    - "sources of complexity in semantic frame parsing for information extraction"
    - "a transition based directed acyclic graph parser for ucca"
    - "word translation without parallel data"
    - "hume human ucca based evaluation of machine translation"
    - "enriching word vectors with subword information"
    - "training very deep networks"

Connected through hyperedge HE-3 (induced by source paper "adapting a framenet semantic parser for spoken language understanding using adversarial learning"):
  with members:
    - "robust semantic parsing with adversarial learning for domain generalization"
    - "adversarial domain adaptation for duplicate question detection"
    - "sources of complexity in semantic frame parsing for information extraction"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "adversarial multi task learning for text classification"
    - "adversarial deep averaging networks for cross lingual sentiment classification"

Connected through hyperedge HE-4 (induced by source paper "spoken conversational search for general knowledge"):
  with members:
    - "coqa a conversational question answering challenge"
    - "quac question answering in context"
    - "end to end neural coreference resolution"
    - "enriching word vectors with subword information"

Question: Please tell me which class the center node 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)