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: "cognitive internet of things a new paradigm beyond connection"
  Abstract: Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named Cognitive Internet of Things (CIoT), to empower the current IoT with a `brain' for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.

Connected through hyperedge HE-1 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"

Connected through hyperedge HE-2 (induced by source paper "earning maximization with quality of charging service guarantee for iot devices"):
  with members:
    - "fair scheduling in resonant beam charging for iot devices"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "wireless charging technologies fundamentals standards and network applications"
    - "wireless charger networking for mobile devices fundamentals standards and applications"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "achieving ethical algorithmic behaviour in the internet of things a review"):
  with members:
    - "clarifying trust in social internet of things"
    - "society in the loop programming the algorithmic social contract"
    - "building ethics into artificial intelligence"
    - "on a formal model of safe and scalable self driving cars"
    - "cyber physical systems security a survey"

Connected through hyperedge HE-4 (induced by source paper "fair scheduling in resonant beam charging for iot devices"):
  with members:
    - "wireless charger networking for mobile devices fundamentals standards and applications"

Connected through hyperedge HE-5 (induced by source paper "adaptive resonant beam charging for intelligent wireless power transfer"):
  with members:
    - "fair scheduling in resonant beam charging for iot devices"

Connected through hyperedge HE-6 (induced by source paper "a survey of machine and deep learning methods for internet of things iot security"):
  with members:
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "machine learning ddos detection for consumer internet of things devices"
    - "lifelong learning for sentiment classification"
    - "iot security techniques based on machine learning"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "detection of unauthorized iot devices using machine learning techniques"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-7 (induced by source paper "enabling cognitive smart cities using big data and machine learning approaches and challenges"):
  with members:
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "learning both weights and connections for efficient neural networks"
    - "context aware computing for the internet of things a survey"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-8 (induced by source paper "lptd achieving lightweight and privacy preserving truth discovery in ciot"):
  with members:
    - "truth finding on the deep web is the problem solved"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"

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)