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: "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
  Abstract: Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability. Conversation modeling will notably benefit from domain knowledge since the relationships between sentences can be clarified due to semantic hints introduced by knowledge. In this paper, a deep neural network is proposed to incorporate background knowledge for conversation modeling. Through a specially designed Recall gate, domain knowledge can be transformed into the extra global memory of Long Short-Term Memory (LSTM), so as to enhance LSTM by cooperating with its local memory to capture the implicit semantic relevance between sentences within conversations. In addition, this paper introduces the loose structured domain knowledge base, which can be built with slight amount of manual work and easily adopted by the Recall gate. Our model is evaluated on the context-oriented response selecting task, and experimental results on both two datasets have shown that our approach is promising for modeling human conversations and building key components of automatic chatting systems.

Connected through hyperedge HE-1 (induced by source paper "conversation generation with concept flow"):
  with members:
    - "the curious case of neural text degeneration"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "neural text generation with unlikelihood training"
    - "target guided open domain conversation"
    - "deep contextualized word representations"
    - "open domain question answering using early fusion of knowledge bases and text"
    - "a dataset for document grounded conversations"
    - "generating informative and diverse conversational responses via adversarial information maximization"

Connected through hyperedge HE-2 (induced by source paper "enriching conversation context in retrieval based chatbots"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "asking clarifying questions in open domain information seeking conversations"
    - "multi task deep neural networks for natural language understanding"
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "neural approaches to conversational ai"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"

Connected through hyperedge HE-3 (induced by source paper "incorporating joint embeddings into goal oriented dialogues with multi task learning"):
  with members:
    - "improving response selection in multi turn dialogue systems by incorporating domain knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "key value retrieval networks for task oriented dialogue"
    - "convolutional 2d knowledge graph embeddings"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "towards end to end reinforcement learning of dialogue agents for information access"

Connected through hyperedge HE-4 (induced by source paper "ranking and selecting multi hop knowledge paths to better predict human needs"):
  with members:
    - "modeling naive psychology of characters in simple commonsense stories"
    - "deep learning for sentiment analysis a survey"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "deep contextualized word representations"
    - "zero shot sequence labeling transferring knowledge from sentences to tokens"
    - "commonsense for generative multi hop question answering tasks"
    - "reflections on sentiment opinion analysis"

Connected through hyperedge HE-5 (induced by source paper "learning to select knowledge for response generation in dialog systems"):
  with members:
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "neural responding machine for short text conversation"
    - "a neural conversational model"

Connected through hyperedge HE-6 (induced by source paper "improving matching models with hierarchical contextualized representations for multi turn response selection"):
  with members:
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "learned in translation contextualized word vectors"
    - "hierarchical recurrent attention network for response generation"
    - "semi supervised sequence tagging with bidirectional language models"

Connected through hyperedge HE-7 (induced by source paper "multi cast attention networks for retrieval based question answering and response prediction"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "multi pointer co attention networks for recommendation"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural ranking models with weak supervision"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "word entity duet representations for document ranking"

Connected through hyperedge HE-8 (induced by source paper "response ranking with deep matching networks and external knowledge in information seeking conversation systems"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "attentive memory networks efficient machine reading for conversational search"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "a persona based neural conversation model"
    - "deep reinforcement learning for dialogue generation"
    - "a deep relevance matching model for ad hoc retrieval"

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)