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: "lstm based mixture of experts for knowledge aware dialogues"
  Abstract: We introduce an LSTM-based method for dynamically integrating several word-prediction experts to obtain a conditional language model which can be good simultaneously at several subtasks. We illustrate this general approach with an application to dialogue where we integrate a neural chat model, good at conversational aspects, with a neural question-answering model, good at retrieving precise information from a knowledge-base, and show how the integration combines the strengths of the independent components. We hope that this focused contribution will attract attention on the benefits of using such mixtures of experts in NLP.

Connected through hyperedge HE-1 (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"
    - "semantically conditioned dialog response generation via hierarchical disentangled self attention"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "knowledge incorporating esim models for response selection in retrieval based dialog systems"):
  with members:
    - "analyzing assumptions in conversation disentanglement research through the lens of a new dataset and model"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"
    - "key value retrieval networks for task oriented dialogue"
    - "a retrieval based dialogue system utilizing utterance and context embeddings"
    - "a knowledge grounded neural conversation model"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "enhanced lstm for natural language inference"
    - "sequential matching network a new architecture for multi turn response selection in retrieval based chatbots"

Connected through hyperedge HE-3 (induced by source paper "lifelong and interactive learning of factual knowledge in dialogues"):
  with members:
    - "towards an engine for lifelong interactive knowledge learning in human machine conversations"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "listen interact and talk learning to speak via interaction"
    - "towards a continuous knowledge learning engine for chatbots"
    - "one shot relational learning for knowledge graphs"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "naturalizing a programming language via interactive learning"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-4 (induced by source paper "breaking the gridlock in mixture of experts consistent and efficient algorithms"):
  with members:
    - "deep mixture of experts via shallow embedding"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "learning one hidden layer neural networks with landscape design"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "attention is all you need"
    - "orthogonalized als a theoretically principled tensor decomposition algorithm for practical use"

Connected through hyperedge HE-5 (induced by source paper "towards an engine for lifelong interactive knowledge learning in human machine conversations"):
  with members:
    - "listen interact and talk learning to speak via interaction"
    - "active one shot learning"
    - "naturalizing a programming language via interactive learning"
    - "a knowledge grounded neural conversation model"
    - "adversarial learning for neural dialogue generation"
    - "data distillation for controlling specificity in dialogue generation"
    - "open world knowledge graph completion"
    - "learning language games through interaction"

Connected through hyperedge HE-6 (induced by source paper "knowledge based end to end memory networks"):
  with members:
    - "a knowledge grounded neural conversation model"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"
    - "building end to end dialogue systems using generative hierarchical neural network models"
    - "evaluating prerequisite qualities for learning end to end dialog systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "a neural network approach to context sensitive generation of conversational responses"
    - "neural responding machine for short text conversation"

Connected through hyperedge HE-7 (induced by source paper "online sequence to sequence active learning for open domain dialogue generation"):
  with members:
    - "key value retrieval networks for task oriented dialogue"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "agent agnostic human in the loop reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a conditional variational framework for dialog generation"
    - "a copy augmented sequence to sequence architecture gives good performance on task oriented dialogue"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "log linear rnns towards recurrent neural networks with flexible prior knowledge"):
  with members:
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "theano a python framework for fast computation of mathematical expressions"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "a neural conversational model"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "neural machine translation by jointly learning to align and translate"
    - "on using very large target vocabulary for neural machine translation"
    - "grammar as a foreign language"

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.AI(Artificial Intelligence)