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: "augmenting end to end dialog systems with commonsense knowledge"
  Abstract: Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human responses in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialog. Our model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models. In the retrieval-based scenario, we propose the Tri-LSTM model to jointly take into account message and commonsense for selecting an appropriate response. Our experiments suggest that the knowledge-augmented models are superior to their knowledge-free counterparts in automatic evaluation.

Connected through hyperedge HE-1 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"

Connected through hyperedge HE-2 (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-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 "thinking globally acting locally distantly supervised global to local knowledge selection for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "refnet a reference aware network for background based conversation"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "improving background based conversation with context aware knowledge pre selection"
    - "learning to select knowledge for response generation in dialog systems"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-5 (induced by source paper "cognitive knowledge graph reasoning for one shot relational learning"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "meta learning for low resource neural machine translation"
    - "relational inductive biases deep learning and graph networks"
    - "variational knowledge graph reasoning"
    - "one shot relational learning for knowledge graphs"

Connected through hyperedge HE-6 (induced by source paper "interactive matching network for multi turn response selection in retrieval based chatbots"):
  with members:
    - "building sequential inference models for end to end response selection"
    - "deep contextualized word representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "end to end neural coreference resolution"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "bilateral multi perspective matching for natural language sentences"
    - "learning to rank question answer pairs using hierarchical recurrent encoder with latent topic clustering"

Connected through hyperedge HE-7 (induced by source paper "memory augmented recurrent networks for dialogue coherence"):
  with members:
    - "towards universal dialogue state tracking"
    - "a hierarchical latent structure for variational conversation modeling"
    - "deep contextualized word representations"
    - "mojitalk generating emotional responses at scale"
    - "memory augmented neural networks with wormhole connections"
    - "pointing the unknown words"
    - "a persona based neural conversation model"
    - "dynamic neural turing machine with soft and hard addressing schemes"

Connected through hyperedge HE-8 (induced by source paper "knowledge incorporating esim models for response selection in retrieval based dialog systems"):
  with members:
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "key value retrieval networks for task oriented dialogue"
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "a knowledge grounded neural conversation model"
    - "enhanced lstm for natural language inference"
    - "lstm based mixture of experts for knowledge aware dialogues"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

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.AI(Artificial Intelligence)