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: "multi task learning for speaker role adaptation in neural conversation models"
  Abstract: Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers' traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.

Connected through hyperedge HE-1 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction"):
  with members:
    - "towards knowledge based personalized product description generation in e commerce"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "an adversarial approach to high quality sentiment controlled neural dialogue generation"
    - "topic guided variational autoencoders for text generation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "knowledge aware conversation generation with reasoning on augmented graph"
    - "reinforcement learning based emotional editing constraint conversation generation"
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"

Connected through hyperedge HE-2 (induced by source paper "ai powered text generation for harmonious human machine interaction current state and future directions"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "learning personalized end to end goal oriented dialog"
    - "multi reward reinforced summarization with saliency and entailment"
    - "learning to generate reviews and discovering sentiment"
    - "key value retrieval networks for task oriented dialogue"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "assigning personality identity to a chatting machine for coherent conversation generation"
    - "automatic generation of natural language explanations"

Connected through hyperedge HE-3 (induced by source paper "consistent dialogue generation with self supervised feature learning"):
  with members:
    - "jointly optimizing diversity and relevance in neural response generation"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "audio visual scene analysis with self supervised multisensory features"

Connected through hyperedge HE-4 (induced by source paper "learning from dialogue after deployment feed yourself chatbot"):
  with members:
    - "beyond turing intelligent agents centered on the user"
    - "bootstrapping conversational agents with weak supervision"
    - "training millions of personalized dialogue agents"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "interactive language acquisition with one shot visual concept learning through a conversational game"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "structuring latent spaces for stylized response generation"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "multi task deep neural networks for natural language understanding"
    - "reinforcement learning based text style transfer without parallel training corpus"
    - "consistent dialogue generation with self supervised feature learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "the natural language decathlon multitask learning as question answering"
    - "the design and implementation of xiaoice an empathetic social chatbot"

Connected through hyperedge HE-6 (induced by source paper "a pre training based personalized dialogue generation model with persona sparse data"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "pretraining methods for dialog context representation learning"
    - "ernie enhanced representation through knowledge integration"
    - "exploiting persona information for diverse generation of conversational responses"
    - "challenges in building intelligent open domain dialog systems"
    - "sentilr linguistic knowledge enhanced language representation for sentiment analysis"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"

Connected through hyperedge HE-7 (induced by source paper "information extraction from scientific literature for method recommendation"):
  with members:
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "knowledge graph completion via complex tensor factorization"
    - "graph based semi supervised conditional random fields for spoken language understanding using unaligned data"
    - "semeval 2017 task 10 scienceie extracting keyphrases and relations from scientific publications"
    - "scientific information extraction with semi supervised neural tagging"
    - "distant supervision for relation extraction beyond the sentence boundary"
    - "lstm based conversation models"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-8 (induced by source paper "a general framework for information extraction using dynamic span graphs"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "neural segmental hypergraphs for overlapping mention recognition"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "deep contextualized word representations"
    - "an annotated corpus for machine reading of instructions in wet lab protocols"

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)