human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "science driven innovations powering mobile product cloud ai vs device ai solutions on smart device"):
  Abstract: Recent years have witnessed the increasing popularity of mobile devices (such as iphone) due to the convenience that it brings to human lives. On one hand, rich user profiling and behavior data (including per-app level, app-interaction level and system-interaction level) from heterogeneous information sources make it possible to provide much better services (such as recommendation, advertisement targeting) to customers, which further drives revenue from understanding users' behaviors and improving user' engagement. In order to delight the customers, intelligent personal assistants (such as Amazon Alexa, Google Home and Google Now) are highly desirable to provide real-time audio, video and image recognition, natural language understanding, comfortable user interaction interface, satisfactory recommendation and effective advertisement targeting. #R##N#This paper presents the research efforts we have conducted on mobile devices which aim to provide much smarter and more convenient services by leveraging statistics and big data science, machine learning and deep learning, user modeling and marketing techniques to bring in significant user growth and user engagement and satisfactions (and happiness) on mobile devices. The developed new features are built at either cloud side or device side, harmonically working together to enhance the current service with the purpose of increasing users' happiness. We illustrate how we design these new features from system and algorithm perspective using different case studies, through which one can easily understand how science driven innovations help to provide much better service in technology and bring more revenue liftup in business. In the meantime, these research efforts have clear scientific contributions and published in top venues, which are playing more and more important roles for mobile AI products.

Includes members:
  Member "deeprebirth accelerating deep neural network execution on mobile devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "a deep reinforcement learning chatbot":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards a human like open domain chatbot")
  Member "deep reinforcement learning for dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "wide deep learning for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "data poisoning attacks on factorization based collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "attention based models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient content based sparse attention with routing transformers")

Question: Please tell me which class the source paper 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.CY(Computers and Society)