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: "few shot generalization across dialogue tasks"
  Abstract: Machine-learning based dialogue managers are able to learn complex behaviors in order to complete a task, but it is not straightforward to extend their capabilities to new domains. We investigate different policies' ability to handle uncooperative user behavior, and how well expertise in completing one task (such as restaurant reservations) can be reapplied when learning a new one (e.g. booking a hotel). We introduce the Recurrent Embedding Dialogue Policy (REDP), which embeds system actions and dialogue states in the same vector space. REDP contains a memory component and attention mechanism based on a modified Neural Turing Machine, and significantly outperforms a baseline LSTM classifier on this task. We also show that both our architecture and baseline solve the bAbI dialogue task, achieving 100% test accuracy.

Connected through hyperedge HE-1 (induced by source paper "domain adaptive dialog generation via meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning for low resource neural machine translation"
    - "sentiment adaptive end to end dialog systems"
    - "adversarial domain adaptation for variational neural language generation in dialogue systems"
    - "prototypical networks for few shot learning"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "learning to reinforcement learn"

Connected through hyperedge HE-2 (induced by source paper "dialogue transformers"):
  with members:
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "training neural response selection for task oriented dialogue systems"
    - "pretraining methods for dialog context representation learning"
    - "sparse sequence to sequence models"
    - "transformer xl attentive language models beyond a fixed length context"
    - "modeling intent dialog policies and response adaptation for goal oriented interactions"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "wizard of wikipedia knowledge powered conversational agents"

Connected through hyperedge HE-3 (induced by source paper "data efficient goal oriented conversation with dialogue knowledge transfer networks"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "addressing objects and their relations the conversational entity dialogue model"
    - "unsupervised dialog structure learning"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "challenging neural dialogue models with natural data memory networks fail on incremental phenomena"
    - "latent intention dialogue models"

Connected through hyperedge HE-4 (induced by source paper "modeling intent dialog policies and response adaptation for goal oriented interactions"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tensor fusion network for multimodal sentiment analysis"
    - "starspace embed all the things"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "attention is all you need"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "rasa open source language understanding and dialogue management"
    - "end to end optimization of task oriented dialogue model with deep 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.CL(Computation and Language)