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: "exploring neural methods for parsing discourse representation structures"
  Abstract: Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics. We present a sequence-to-sequence neural semantic parser that is able to produce Discourse Representation Structures (DRSs) for English sentences with high accuracy, outperforming traditional DRS parsers. To facilitate the learning of the output, we represent DRSs as a sequence of flat clauses and introduce a method to verify that produced DRSs are well-formed and interpretable. We compare models using characters and words as input and see (somewhat surprisingly) that the former performs better than the latter. We show that eliminating variable names from the output using De Bruijn-indices increases parser performance. Adding silver training data boosts performance even further.

Connected through hyperedge HE-1 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "roberta a robustly optimized bert pretraining approach"
    - "joint slot filling and intent detection via capsule neural networks"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "semantic parsing for task oriented dialog using hierarchical representations"

Connected through hyperedge HE-2 (induced by source paper "a survey of cross lingual features for zero shot cross lingual semantic parsing"):
  with members:
    - "polyglot semantic role labeling"
    - "cross lingual semantic parsing"
    - "evaluating scoped meaning representations"
    - "coarse to fine decoding for neural semantic parsing"
    - "neural networks for cross lingual negation scope detection"
    - "universal semantic parsing"
    - "cross lingual abstract meaning representation parsing"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "semantic graph parsing with recurrent neural network dag grammars"):
  with members:
    - "learning deep generative models of graphs"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "amr dependency parsing with a typed semantic algebra"
    - "amr parsing as graph prediction with latent alignment"
    - "towards universal semantic tagging"
    - "robust incremental neural semantic graph parsing"
    - "the parallel meaning bank towards a multilingual corpus of translations annotated with compositional meaning representations"
    - "incorporating copying mechanism in sequence to sequence learning"

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)