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: "fast and scalable expansion of natural language understanding functionality for intelligent agents"
  Abstract: Fast expansion of natural language functionality of intelligent virtual agents is critical for achieving engaging and informative interactions. However, developing accurate models for new natural language domains is a time and data intensive process. We propose efficient deep neural network architectures that maximally re-use available resources through transfer learning. Our methods are applied for expanding the understanding capabilities of a popular commercial agent and are evaluated on hundreds of new domains, designed by internal or external developers. We demonstrate that our proposed methods significantly increase accuracy in low resource settings and enable rapid development of accurate models with less data.

Connected through hyperedge HE-1 (induced by source paper "genie a generator of natural language semantic parsers for virtual assistant commands"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "data augmentation for spoken language understanding via joint variational generation"
    - "interactive semantic parsing for if then recipes via hierarchical reinforcement learning"
    - "deep contextualized word representations"
    - "learned in translation contextualized word vectors"
    - "attend to you personalized image captioning with context sequence memory networks"

Connected through hyperedge HE-2 (induced by source paper "robust zero shot cross domain slot filling with example values"):
  with members:
    - "zero shot adaptive transfer for conversational language understanding"
    - "multi source domain adaptation with mixture of experts"
    - "contextual slot carryover for disparate schemas"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "zero shot user intent detection via capsule neural networks"

Connected through hyperedge HE-3 (induced by source paper "unsupervised transfer learning for spoken language understanding in intelligent agents"):
  with members:
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "empower sequence labeling with task aware neural language model"
    - "enriching word vectors with subword information"
    - "exploring the limits of language modeling"
    - "skip thought vectors"
    - "semi supervised sequence learning"
    - "named entity recognition with bidirectional lstm cnns"

Connected through hyperedge HE-4 (induced by source paper "zero shot adaptive transfer for conversational language understanding"):
  with members:
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "end to end neural coreference resolution"
    - "zero shot transfer learning for event extraction"
    - "domain adaptation of recurrent neural networks for natural language understanding"
    - "joint online spoken language understanding and language modeling with recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (induced by source paper "bootstrapping conversational agents with weak supervision"):
  with members:
    - "learning the structure of generative models without labeled data"

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)