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: "semantically conditioned dialog response generation via hierarchical disentangled self attention"
  Abstract: Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be difficult because the possible combinations of semantic inputs grow exponentially with the number of domains. To alleviate such scalability issue, we exploit the structure of dialog acts to build a multi-layer hierarchical graph, where each act is represented as a root-to-leaf route on the graph. Then, we incorporate such graph structure prior as an inductive bias to build a hierarchical disentangled self-attention network, where we disentangle attention heads to model designated nodes on the dialog act graph. By activating different (disentangled) heads at each layer, combinatorially many dialog act semantics can be modeled to control the neural response generation. On the large-scale Multi-Domain-WOZ dataset, our model can yield a significant improvement over the baselines on various automatic and human evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "few shot natural language generation for task oriented dialog"):
  with members:
    - "towards a human like open domain chatbot"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "plug and play language models a simple approach to controlled text generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "defending against neural fake news"
    - "dialogpt large scale generative pre training for conversational response generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems"):
  with members:
    - "plato dialogue system a flexible conversational ai research platform"
    - "crosswoz a large scale chinese cross domain task oriented dialogue dataset"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "sumbt slot utterance matching for universal and scalable belief tracking"
    - "towards scalable multi domain conversational agents the schema guided dialogue dataset"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "guided dialog policy learning reward estimation for multi domain task oriented dialog"

Connected through hyperedge HE-3 (induced by source paper "retrieval based goal oriented dialogue generation"):
  with members:
    - "scaling multi domain dialogue state tracking via query reformulation"
    - "on extractive and abstractive neural document summarization with transformer language models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "towards universal dialogue state tracking"
    - "a strong baseline for question relevancy ranking"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "sequence to sequence learning for task oriented dialogue with dialogue state representation"
    - "scalable multi domain dialogue state tracking"

Connected through hyperedge HE-4 (induced by source paper "retrospective and prospective mixture of generators for task oriented dialogue response generation"):
  with members:
    - "structured fusion networks for dialog"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "a modular task oriented dialogue system using a neural mixture of experts"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "memory augmented dialogue management for task oriented dialogue systems"
    - "multi source domain adaptation with mixture of experts"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "granger causal attentive mixtures of experts learning important features with neural networks"

Connected through hyperedge HE-5 (induced by source paper "structured fusion networks for dialog"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "pretraining methods for dialog context representation learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "zero shot dialog generation with cross domain latent actions"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"
    - "cross domain dialogue policy transfer via simultaneous speech act and slot alignment"
    - "toward controlled generation of text"

Connected through hyperedge HE-6 (induced by source paper "task oriented dialog systems that consider multiple appropriate responses under the same context"):
  with members:
    - "structured fusion networks for dialog"
    - "the curious case of neural text degeneration"
    - "convlab multi domain end to end dialog system platform"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "learning end to end goal oriented dialog with multiple answers"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multiwoz a large scale multi domain wizard of oz dataset for task oriented dialogue modelling"

Connected through hyperedge HE-7 (induced by source paper "mala cross domain dialogue generation with action learning"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "carl aggregated search with context aware module embedding learning"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "unsupervised text style transfer using language models as discriminators"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"

Connected through hyperedge HE-8 (induced by source paper "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "the curious case of neural text degeneration"
    - "training neural response selection for task oriented dialogue systems"
    - "a repository of conversational datasets"
    - "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"

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)