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: "learning to attend copy and generate for session based query suggestion"
  Abstract: Users try to articulate their complex information needs during search sessions by reformulating their queries. To make this process more effective, search engines provide related queries to help users in specifying the information need in their search process. In this paper, we propose a customized sequence-to-sequence model for session-based query suggestion. In our model, we employ a query-aware attention mechanism to capture the structure of the session context. is enables us to control the scope of the session from which we infer the suggested next query, which helps not only handle the noisy data but also automatically detect session boundaries. Furthermore, we observe that, based on the user query reformulation behavior, within a single session a large portion of query terms is retained from the previously submitted queries and consists of mostly infrequent or unseen terms that are usually not included in the vocabulary. We therefore empower the decoder of our model to access the source words from the session context during decoding by incorporating a copy mechanism. Moreover, we propose evaluation metrics to assess the quality of the generative models for query suggestion. We conduct an extensive set of experiments and analysis. e results suggest that our model outperforms the baselines both in terms of the generating queries and scoring candidate queries for the task of query suggestion.

Connected through hyperedge HE-1 (induced by source paper "pre training for query rewriting in a spoken language understanding system"):
  with members:
    - "knowledge distillation from internal representations"
    - "training neural response selection for task oriented dialogue systems"
    - "feedback based self learning in large scale conversational ai agents"
    - "learning from past mistakes improving automatic speech recognition output via noisy clean phrase context modeling"
    - "towards end to end spoken language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-2 (induced by source paper "using image captions and multitask learning for recommending query reformulations"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-3 (induced by source paper "modeling information need of users in search sessions"):
  with members:
    - "a hierarchical recurrent encoder decoder for generative context aware query suggestion"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-5 (induced by source paper "subword language model for query auto completion"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "personalized neural language models for real world query auto completion"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to discover ground and use words with segmental neural language models"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "personalized language model for query auto completion"
    - "distilling knowledge for search based structured prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-6 (induced by source paper "context attentive document ranking and query suggestion"):
  with members:
    - "attention based hierarchical neural query suggestion"
    - "retrieve and read multi task learning of information retrieval and reading comprehension"
    - "a click sequence model for web search"
    - "match tensor a deep relevance model for search"
    - "neural ranking models with weak supervision"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"
    - "learning natural language inference using bidirectional lstm model and inner attention"

Connected through hyperedge HE-7 (induced by source paper "a dataset for resolving referring expressions in spoken dialogue via contextual query rewrites cqr"):
  with members:
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "contextual slot carryover for disparate schemas"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "an efficient approach to encoding context for spoken language understanding"
    - "key value retrieval networks for task oriented dialogue"
    - "scalable multi domain dialogue state tracking"
    - "opennmt open source toolkit for neural machine translation"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-8 (induced by source paper "global to local memory pointer networks for task oriented dialogue"):
  with members:
    - "global locally self attentive dialogue state tracker"
    - "attention is all you need"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "generative encoder decoder models for task oriented spoken dialog systems with chatting capability"
    - "natural language generation in dialogue using lexicalized and delexicalized data"
    - "memory enhanced decoder for neural machine translation"
    - "a network based end to end trainable task oriented dialogue system"
    - "key value memory networks for directly reading documents"

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.IR(Information Retrieval)