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: "deep learning for iot big data and streaming analytics a survey"
  Abstract: In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely Deep Learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "machine learning for intelligent optical networks a comprehensive survey"):
  with members:
    - "adaptive federated learning in resource constrained edge computing systems"
    - "artificial intelligence ai methods in optical networks a comprehensive survey"
    - "machine learning for networking workflow advances and opportunities"
    - "deep reinforcement learning a brief survey"
    - "federated learning of deep networks using model averaging"
    - "asynchronous methods for deep reinforcement learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "adaptive anomaly detection for iot data in hierarchical edge computing"):
  with members:
    - "distributed anomaly detection using autoencoder neural networks in wsn for iot"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-4 (induced by source paper "a scalable iot fog framework for urban sound sensing"):
  with members:
    - "using cloud and fog computing for large scale iot based urban sound classification"
    - "fog computing survey of trends architectures requirements and research directions"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "hierarchical fog cloud computing for iot systems a computation offloading game"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-6 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-7 (induced by source paper "a survey on consensus mechanisms and mining strategy management in blockchain networks"):
  with members:
    - "decentralization in bitcoin and ethereum networks"
    - "information and communications technologies for sustainable development goals state of the art needs and perspectives"
    - "a scale out blockchain for value transfer with spontaneous sharding"
    - "a survey on security and privacy issues of bitcoin"
    - "on cyber risk management of blockchain networks a game theoretic approach"
    - "scaling nakamoto consensus to thousands of transactions per second"
    - "competitive data trading in wireless powered internet of things iot crowdsensing systems with blockchain"
    - "blockchained on device federated learning"

Connected through hyperedge HE-8 (induced by source paper "deep learning at the edge"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "rdeepsense reliable deep mobile computing models with uncertainty estimations"

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.NI(Networking and Internet Architecture)