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: "learning to select knowledge for response generation in dialog systems"
  Abstract: End-to-end neural models for intelligent dialogue systems suffer from the problem of generating uninformative responses. Various methods were proposed to generate more informative responses by leveraging external knowledge. However, few previous work has focused on selecting appropriate knowledge in the learning process. The inappropriate selection of knowledge could prohibit the model from learning to make full use of the knowledge. Motivated by this, we propose an end-to-end neural model which employs a novel knowledge selection mechanism where both prior and posterior distributions over knowledge are used to facilitate knowledge selection. Specifically, a posterior distribution over knowledge is inferred from both utterances and responses, and it ensures the appropriate selection of knowledge during the training process. Meanwhile, a prior distribution, which is inferred from utterances only, is used to approximate the posterior distribution so that appropriate knowledge can be selected even without responses during the inference process. Compared with the previous work, our model can better incorporate appropriate knowledge in response generation. Experiments on both automatic and human evaluation verify the superiority of our model over previous baselines.

Connected through hyperedge HE-1 (induced by source paper "low resource knowledge grounded dialogue generation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "mass masked sequence to sequence pre training for language generation"
    - "disentangling factors of variation using few labels"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "deep contextualized word representations"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue"):
  with members:
    - "long and diverse text generation with planning based hierarchical variational model"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "visualizing and understanding the effectiveness of bert"

Connected through hyperedge HE-3 (induced by source paper "refnet a reference aware network for background based conversation"):
  with members:
    - "incremental transformer with deliberation decoder for document grounded conversations"
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "challenges in building intelligent open domain dialog systems"
    - "knowledge aware conversation generation with explainable reasoning on augmented graphs"
    - "first principle study on honeycomb fluorated inte monolayer with large rashba spin splitting and direct bandgap"
    - "improving background based conversation with context aware knowledge pre selection"
    - "knowledge aware conversation generation with reasoning on augmented graph"

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"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a dataset for document grounded conversations"

Connected through hyperedge HE-5 (induced by source paper "know more about each other evolving dialogue strategy via compound assessment"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "chat more if you like dynamic cue words planning to flow longer conversations"
    - "a knowledge grounded neural conversation model"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-6 (induced by source paper "improving background based conversation with context aware knowledge pre selection"):
  with members:
    - "improving neural response diversity with frequency aware cross entropy loss"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "towards exploiting background knowledge for building conversation systems"
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-7 (induced by source paper "improving knowledge aware dialogue generation via knowledge base question answering"):
  with members:
    - "response generation by context aware prototype editing"
    - "a knowledge grounded neural conversation model"
    - "improved neural relation detection for knowledge base question answering"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "deep reinforcement learning for dialogue generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "question answering on freebase via relation extraction and textual evidence"
    - "building end to end dialogue systems using generative hierarchical neural network models"

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)