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: "dialogue manager domain adaptation using gaussian process reinforcement learning"
  Abstract: Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. By using speech as the primary communication medium, a computer interface can facilitate swift, human-like acquisition of information. In recent years, speech interfaces have become ever more popular, as is evident from the rise of personal assistants such as Siri, Google Now, Cortana and Amazon Alexa. Recently, data-driven machine learning methods have been applied to dialogue modelling and the results achieved for limited-domain applications are comparable to or outperform traditional approaches. Methods based on Gaussian processes are particularly effective as they enable good models to be estimated from limited training data. Furthermore, they provide an explicit estimate of the uncertainty which is particularly useful for reinforcement learning. This article explores the additional steps that are necessary to extend these methods to model multiple dialogue domains. We show that Gaussian process reinforcement learning is an elegant framework that naturally supports a range of methods, including prior knowledge, Bayesian committee machines and multi-agent learning, for facilitating extensible and adaptable dialogue systems.

Connected through hyperedge HE-1 (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"
    - "end to end joint learning of natural language understanding and dialogue manager"

Connected through hyperedge HE-2 (induced by source paper "ld sds towards an expressive spoken dialogue system based on linked data"):
  with members:
    - "a frame tracking model for memory enhanced dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "language generation with recurrent generative adversarial networks without pre training"
    - "a network based end to end trainable task oriented dialogue system"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "neural text generation from structured data with application to the biography domain"
    - "natural language generation enhances human decision making with uncertain information"
    - "end to end joint learning of natural language understanding and dialogue manager"

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)