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: "domain adaptive dialog generation via meta learning"
  Abstract: Domain adaptation is an essential task in dialog system building because there are so many new dialog tasks created for different needs every day. Collecting and annotating training data for these new tasks is costly since it involves real user interactions. We propose a domain adaptive dialog generation method based on meta-learning (DAML). DAML is an end-to-end trainable dialog system model that learns from multiple rich-resource tasks and then adapts to new domains with minimal training samples. We train a dialog system model using multiple rich-resource single-domain dialog data by applying the model-agnostic meta-learning algorithm to dialog domain. The model is capable of learning a competitive dialog system on a new domain with only a few training examples in an efficient manner. The two-step gradient updates in DAML enable the model to learn general features across multiple tasks. We evaluate our method on a simulated dialog dataset and achieve state-of-the-art performance, which is generalizable to new tasks.

Connected through hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system"):
  with members:
    - "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "global to local memory pointer networks for task oriented dialogue"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "unsupervised dialog structure learning"
    - "cosql a conversational text to sql challenge towards cross domain natural language interfaces to databases"
    - "task oriented conversation generation using heterogeneous memory networks"

Connected through hyperedge HE-2 (induced by source paper "zero shot cross lingual transfer with meta learning"):
  with members:
    - "x wikire a large multilingual resource for relation extraction as machine comprehension"
    - "investigating meta learning algorithms for low resource natural language understanding tasks"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "on first order meta learning algorithms"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "from phonology to syntax unsupervised linguistic typology at different levels with language embeddings"
    - "xnli evaluating cross lingual sentence representations"
    - "glue a multi task benchmark and analysis platform for natural language understanding"

Connected through hyperedge HE-3 (induced by source paper "investigating meta learning algorithms for low resource natural language understanding tasks"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "linguistic knowledge and transferability of contextual representations"
    - "on first order meta learning algorithms"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"

Connected through hyperedge HE-4 (induced by source paper "how to build user simulators to train rl based dialog systems"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "unsupervised dialog structure learning"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "decoupling strategy and generation in negotiation dialogues"
    - "building a conversational agent overnight with dialogue self play"
    - "sentiment adaptive end to end dialog systems"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "mala cross domain dialogue generation with action learning"):
  with members:
    - "carl aggregated search with context aware module embedding learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised text style transfer using language models as discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-6 (induced by source paper "table to text natural language generation with unseen schemas"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "domain adaptation of neural machine translation by lexicon induction"
    - "step by step separating planning from realization in neural data to text generation"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "natural language to structured query generation via meta learning"
    - "data to text generation with content selection and planning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-7 (induced by source paper "enhanced meta learning for cross lingual named entity recognition with minimal resources"):
  with members:
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "cross lingual language model pretraining"
    - "chinese ner using lattice lstm"
    - "meta learning for low resource neural machine translation"
    - "deep contextualized word representations"
    - "natural language to structured query generation via meta learning"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "learning to customize language model for generation based dialog systems"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "meta transfer learning for few shot learning"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "smash one shot model architecture search through hypernetworks"
    - "a persona based neural conversation model"

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)