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: "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
  Abstract: In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding- based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.

Connected through hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems"):
  with members:
    - "a hybrid retrieval generation neural conversation model"
    - "attentive history selection for conversational question answering"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "a hybrid retrieval generation neural conversation model"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "response generation by context aware prototype editing"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"

Connected through hyperedge HE-3 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "learning how to active learn a deep reinforcement learning approach"

Connected through hyperedge HE-4 (induced by source paper "product aware answer generation in e commerce question answering"):
  with members:
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "learning to remember translation history with a continuous cache"
    - "ruber an unsupervised method for automatic evaluation of open domain dialog systems"
    - "an abstractive approach to question answering"
    - "query based abstractive summarization using neural networks"
    - "get to the point summarization with pointer generator networks"
    - "improved training of wasserstein gans"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Connected through hyperedge HE-5 (induced by source paper "review driven answer generation for product related questions in e commerce"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "flexible and creative chinese poetry generation using neural memory"
    - "convolutional sequence to sequence learning"
    - "interactive attention networks for aspect level sentiment classification"
    - "deep reinforcement learning for dialogue generation"
    - "sequence to backward and forward sequences a content introducing approach to generative short text conversation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "topic aware neural response generation"

Connected through hyperedge HE-6 (induced by source paper "joint learning of answer selection and answer summary generation in community question answering"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "duorc towards complex language understanding with paraphrased reading comprehension"
    - "neural document summarization by jointly learning to score and select sentences"
    - "wikihow a large scale text summarization dataset"
    - "joint multitask learning for community question answering using task specific embeddings"
    - "multi task learning with multi view attention for answer selection and knowledge base question answering"

Connected through hyperedge HE-7 (induced by source paper "hashing based answer selection"):
  with members:
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "question answering through transfer learning from large fine grained supervision data"
    - "attention is all you need"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "supervised and unsupervised transfer learning for question answering"
    - "neural domain adaptation for biomedical question answering"
    - "hashnet deep learning to hash by continuation"

Connected through hyperedge HE-8 (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"
    - "semantic frame parsing for information extraction the calor corpus"
    - "linguistically informed self attention for semantic role labeling"
    - "deep semantic role labeling with self attention"
    - "adversarial multi task learning for text classification"
    - "word translation without parallel 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)