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: "disentangling language and knowledge in task oriented dialogs"
  Abstract: The Knowledge Base (KB) used for real-world applications, such as booking a movie or restaurant reservation, keeps changing over time. End-to-end neural networks trained for these task-oriented dialogs are expected to be immune to any changes in the KB. However, existing approaches breakdown when asked to handle such changes. We propose an encoder-decoder architecture (BoSsNet) with a novel Bag-of-Sequences (BoSs) memory, which facilitates the disentangled learning of the response's language model and its knowledge incorporation. Consequently, the KB can be modified with new knowledge without a drop in interpretability. We find that BoSsNet outperforms state-of-the-art models, with considerable improvements (> 10\%) on bAbI OOV test sets and other human-human datasets. We also systematically modify existing datasets to measure disentanglement and show BoSsNet to be robust to KB modifications.

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"
    - "learning to select knowledge for response generation in dialog systems"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "towards vqa models that can read"):
  with members:
    - "bilinear attention networks"
    - "learning to count objects in natural images for visual question answering"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "rosetta large scale system for text detection and recognition in images"
    - "dvqa understanding data visualizations via question answering"
    - "figureqa an annotated figure dataset for visual reasoning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-3 (induced by source paper "deepcopy grounded response generation with hierarchical pointer networks"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "a knowledge grounded neural conversation model"
    - "get to the point summarization with pointer generator networks"
    - "a persona based neural conversation model"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "dynamic coattention networks for question answering"
    - "multi source neural translation"

Connected through hyperedge HE-4 (induced by source paper "reasoning over rdf knowledge bases using deep learning"):
  with members:
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "ontology reasoning with deep neural networks"
    - "learning to compute word embeddings on the fly"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "end to end differentiable proving"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

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.LG(Machine Learning)