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: "extending neural generative conversational model using external knowledge sources"
  Abstract: The use of connectionist approaches in conversational agents has been progressing rapidly due to the availability of large corpora. However current generative dialogue models often lack coherence and are content poor. This work proposes an architecture to incorporate unstructured knowledge sources to enhance the next utterance prediction in chit-chat type of generative dialogue models. We focus on Sequence-to-Sequence (Seq2Seq) conversational agents trained with the Reddit News dataset, and consider incorporating external knowledge from Wikipedia summaries as well as from the NELL knowledge base. Our experiments show faster training time and improved perplexity when leveraging external knowledge.

Connected through hyperedge HE-1 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue"):
  with members:
    - "long and diverse text generation with planning based hierarchical variational model"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-2 (induced by source paper "refnet a reference aware network for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "challenges in building intelligent open domain dialog systems"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "first principle study on honeycomb fluorated inte monolayer with large rashba spin splitting and direct bandgap"
    - "improving background based conversation with context aware knowledge pre selection"
    - "learning to select knowledge for response generation in dialog systems"

Connected through hyperedge HE-3 (induced by source paper "knowledge enriched transformer for emotion detection in textual conversations"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "global to local memory pointer networks for task oriented dialogue"
    - "ntuer at semeval 2019 task 3 emotion classification with word and sentence representations in rcnn"
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "transformer xl attentive language models beyond a fixed length context"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-4 (induced by source paper "what makes a good conversation how controllable attributes affect human judgments"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "topic based evaluation for conversational bots"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"
    - "generating more interesting responses in neural conversation models with distributional constraints"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "incremental transformer with deliberation decoder for document grounded conversations"):
  with members:
    - "knowledge grounded response generation with deep attentional latent variable model"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "modeling coherence for discourse neural machine translation"
    - "improving the transformer translation model with document level context"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "coqa a conversational question answering challenge"
    - "wizard of wikipedia knowledge powered conversational agents"

Connected through hyperedge HE-6 (induced by source paper "enhancing recurrent neural networks with sememes"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "towards a robust deep neural network in text domain a survey"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "jointly predicting predicates and arguments in neural semantic role labeling"

Connected through hyperedge HE-7 (induced by source paper "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "wizard of wikipedia knowledge powered conversational agents"

Connected through hyperedge HE-8 (induced by source paper "wizard of wikipedia knowledge powered conversational agents"):
  with members:
    - "training millions of personalized dialogue agents"
    - "universal sentence encoder"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "towards exploiting background knowledge for building conversation systems"
    - "quac question answering in context"
    - "a knowledge grounded neural conversation model"
    - "efficient natural language response suggestion for smart reply"
    - "starspace embed all the things"

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)