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: "end to end joint learning of natural language understanding and dialogue manager"
  Abstract: Natural language understanding and dialogue policy learning are both essential in conversational systems that predict the next system actions in response to a current user utterance. Conventional approaches aggregate separate models of natural language understanding (NLU) and system action prediction (SAP) as a pipeline that is sensitive to noisy outputs of error-prone NLU. To address the issues, we propose an end-to-end deep recurrent neural network with limited contextual dialogue memory by jointly training NLU and SAP on DSTC4 multi-domain human-human dialogues. Experiments show that our proposed model significantly outperforms the state-of-the-art pipeline models for both NLU and SAP, which indicates that our joint model is capable of mitigating the affects of noisy NLU outputs, and NLU model can be refined by error flows backpropagating from the extra supervised signals of system actions.

Connected through hyperedge HE-1 (induced by source paper "plato dialogue system a flexible conversational ai research platform"):
  with members:
    - "moss end to end dialog system framework with modular supervision"
    - "collaborative multi agent dialogue model training via reinforcement learning"
    - "convlab multi domain end to end dialog system platform"
    - "huggingface s transformers state of the art natural language processing"
    - "nemo a toolkit for building ai applications using neural modules"
    - "adversarial learning of task oriented neural dialog models"
    - "multi task learning for joint language understanding and dialogue state tracking"
    - "detecting offensive content in open domain conversations using two stage semi supervision"

Connected through hyperedge HE-2 (induced by source paper "semi supervised neural text generation by joint learning of natural language generation and natural language understanding models"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "char2char generation with reranking for the e2e nlg challenge"
    - "end to end feedback loss in speech chain framework via straight through estimator"
    - "end to end content and plan selection for data to text generation"
    - "a deep ensemble model with slot alignment for sequence to sequence natural language generation"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "neural machine translation with gumbel greedy decoding"
    - "listening while speaking speech chain by deep learning"

Connected through hyperedge HE-3 (induced by source paper "user modeling for task oriented dialogues"):
  with members:
    - "neural user simulation for corpus based policy optimisation for spoken dialogue systems"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"
    - "end to end optimization of task oriented dialogue model with deep reinforcement learning"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "a hierarchical neural autoencoder for paragraphs and documents"

Connected through hyperedge HE-4 (induced by source paper "evorus a crowd powered conversational assistant built to automate itself over time"):
  with members:
    - "is there anything else i can help you with challenges in deploying an on demand crowd powered conversational agent"
    - "real time on demand crowd powered entity extraction"
    - "end to end task completion neural dialogue systems"
    - "dialport connecting the spoken dialog research community to real user data"
    - "a persona based neural conversation model"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "dialogue manager domain adaptation using gaussian process reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "towards dialogue based navigation with multivariate adaptation driven by intention and politeness for social robots"):
  with members:
    - "discourse wizard discovering deep discourse structure in your conversation with rnns"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"
    - "sentiment adaptive end to end dialog systems"
    - "enabling a pepper robot to provide automated and interactive tours of a robotics laboratory"
    - "setting up pepper for autonomous navigation and personalized interaction with users"
    - "dialogue act sequence labeling using hierarchical encoder with crf"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-6 (induced by source paper "sample efficient actor critic reinforcement learning with supervised data for dialogue management"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deep q learning from demonstrations"
    - "sample efficient deep reinforcement learning for dialog control"
    - "deep reinforcement learning for dialogue generation"
    - "continuously learning neural dialogue management"
    - "policy networks with two stage training for dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "learning end to end goal oriented dialog"

Connected through hyperedge HE-7 (induced by source paper "a survey on network embedding"):
  with members:
    - "fast warped graph embedding unifying framework and one click algorithm"
    - "heterogeneous information network embedding for meta path based proximity"
    - "bibliographic analysis with the citation network topic model"
    - "higher order organization of complex networks"
    - "a general framework for content enhanced network representation learning"
    - "deepwalk online learning of social representations"
    - "graph based anomaly detection and description a survey"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-8 (induced by source paper "dynamic time aware attention to speaker roles and contexts for spoken language understanding"):
  with members:
    - "end to end task completion neural dialogue systems"
    - "speaker role contextual modeling for language understanding and dialogue policy learning"
    - "knowledge as a teacher knowledge guided structural attention networks"
    - "a network based end to end trainable task oriented dialogue system"
    - "towards end to end reinforcement learning of dialogue agents for information access"
    - "dynamic memory networks for visual and textual question answering"
    - "learning end to end goal oriented dialog"
    - "end to end memory networks"

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)