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: "toward continual learning for conversational agents"
  Abstract: While end-to-end neural conversation models have led to promising advances in reducing hand-crafted features and errors induced by the traditional complex system architecture, they typically require an enormous amount of data. Previous studies adopted a hybrid approach with knowledge-based components to abstract out domain-specific things or to augment data to cover more diverse patterns. On the contrary, we propose to directly address the problem using the recent development in the space of continual learning for neural models. Specifically, we adopt a domain-independent neural conversational model and introduce a novel neural continual learning algorithm that allows the conversational agent to accumulate skills across different tasks in a data-efficient way. To the best of our knowledge, this is the first work that applies continual learning for conversation systems. We verified the efficacy of our method through a conversational skill transfer from synthetic dialogs or human-human dialogs to human-computer conversations in a customer support domain.

Connected through hyperedge HE-1 (induced by source paper "transferable multi domain state generator for task oriented dialogue systems"):
  with members:
    - "global to local memory pointer networks for task oriented dialogue"
    - "the natural language decathlon multitask learning as question answering"
    - "towards universal dialogue state tracking"
    - "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"
    - "neural approaches to conversational ai"
    - "toward scalable neural dialogue state tracking model"
    - "large scale multi domain belief tracking with knowledge sharing"

Connected through hyperedge HE-2 (induced by source paper "lamol language modeling for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "on training recurrent neural networks for lifelong learning"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"

Connected through hyperedge HE-3 (induced by source paper "lamal language modeling is all you need for lifelong language learning"):
  with members:
    - "episodic memory in lifelong language learning"
    - "continual learning for sentence representations using conceptors"
    - "lifelong domain word embedding via meta learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "lifelong learning for sentiment classification"
    - "on training recurrent neural networks for lifelong learning"
    - "the natural language decathlon multitask learning as question answering"
    - "efficient lifelong learning with a gem"

Connected through hyperedge HE-4 (induced by source paper "psycholinguistics meets continual learning measuring catastrophic forgetting in visual question answering"):
  with members:
    - "learning and evaluating general linguistic intelligence"
    - "lifelong learning for sentiment classification"
    - "continuous learning in single incremental task scenarios"
    - "riemannian walk for incremental learning understanding forgetting and intransigence"
    - "inferring and executing programs for visual reasoning"
    - "continual learning through synaptic intelligence"
    - "film visual reasoning with a general conditioning layer"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-5 (induced by source paper "continual learning for sentence representations using conceptors"):
  with members:
    - "zero training sentence embedding via orthogonal basis"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal sentence encoder"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "the rapidly changing landscape of conversational agents"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "continual learning through synaptic intelligence"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-6 (induced by source paper "the rapidly changing landscape of conversational agents"):
  with members:
    - "quantized dialog language model for goal oriented conversational systems"
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "not all dialogues are created equal instance weighting for neural conversational models"
    - "a knowledge grounded neural conversation model"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "adversarial learning for neural dialogue generation"
    - "augmenting end to end dialog systems with commonsense knowledge"

Connected through hyperedge HE-7 (induced by source paper "goal oriented chatbot dialog management bootstrapping with transfer learning"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "a network based end to end trainable task oriented dialogue system"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "a user simulator for task completion dialogues"
    - "continuously learning neural dialogue management"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"

Connected through hyperedge HE-8 (induced by source paper "improving robustness of neural dialog systems in a data efficient way with turn dropout"):
  with members:
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "key value retrieval networks for task oriented dialogue"
    - "frames a corpus for adding memory to goal oriented dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "neural sentence embedding using only in domain sentences for out of domain sentence detection in dialog systems"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "learning end to end goal oriented dialog"
    - "generating sentences from a continuous space"

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)