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: "iso standard domain independent dialogue act tagging for conversational agents"
  Abstract: Dialogue Act (DA) tagging is crucial for spoken language understanding systems, as it provides a general representation of speakers' intents, not bound to a particular dialogue system. Unfortunately, publicly available data sets with DA annotation are all based on different annotation schemes and thus incompatible with each other. Moreover, their schemes often do not cover all aspects necessary for open-domain human-machine interaction. In this paper, we propose a methodology to map several publicly available corpora to a subset of the ISO standard, in order to create a large task-independent training corpus for DA classification. We show the feasibility of using this corpus to train a domain-independent DA tagger testing it on out-of-domain conversational data, and argue the importance of training on multiple corpora to achieve robustness across different DA categories.

Connected through hyperedge HE-1 (induced by source paper "natural language generation at scale a case study for open domain question answering"):
  with members:
    - "curate and generate a corpus and method for joint control of semantics and style in neural nlg"
    - "constrained decoding for neural nlg from compositional representations in task oriented dialogue"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "findings of the e2e nlg challenge"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-2 (induced by source paper "midas a dialog act annotation scheme for open domain human machine spoken conversations"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "contextual topic modeling for dialog systems"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "advances in pre training distributed word representations"
    - "exploiting sentence and context representations in deep neural models for spoken language understanding"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "towards universal dialogue act tagging for task oriented dialogues"):
  with members:
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "edina building an open domain socialbot with self dialogues"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "enriching word vectors with subword information"
    - "sequential short text classification with recurrent and convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "multi lingual dialogue act recognition with deep learning methods"):
  with members:
    - "linear transformations for cross lingual semantic textual similarity"
    - "robust multilingual named entity recognition with shallow semi supervised features"
    - "sequential short text classification with recurrent and convolutional neural networks"
    - "convolutional neural networks for sentence classification"
    - "efficient estimation of word representations in vector space"

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)