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: "machine learning in wireless sensor networks algorithms strategies and applications"
  Abstract: Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

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 learning for iot big data and streaming analytics a survey"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "optimal pricing of internet of things a machine learning approach"):
  with members:
    - "hybrid pricing for mobile collaborative internet access"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "mobile big data analytics using deep learning and apache spark"
    - "context aware computing for the internet of things a survey"
    - "a theory of pricing private data"
    - "a survey of smart data pricing past proposals current plans and future trends"

Connected through hyperedge HE-4 (induced by source paper "graph optimization approach to range based localization"):
  with members:
    - "kervolutional neural networks"
    - "kernel cross correlator"
    - "ultra wideband aided fast localization and mapping system"
    - "non iterative slam"

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 "thirty years of machine learning the road to pareto optimal next generation wireless networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "budget constrained multi armed bandits with multiple plays"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-7 (induced by source paper "big data analytics for large scale wireless networks challenges and opportunities"):
  with members:
    - "blockchain for internet of things a survey"
    - "big data analytics for manufacturing internet of things opportunities challenges and enabling technologies"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "stealing hyperparameters in machine learning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "a survey of multi objective optimization in wireless sensor networks metrics algorithms and open problems"

Connected through hyperedge HE-8 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "machine learning ddos detection for consumer internet of things devices"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"

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)