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: "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
  Abstract: Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attention from global researchers and engineers, which can significantly bridge the capacity of cloud and requirement of devices by the network edges, and thus can accelerate content delivery and improve the quality of mobile services. In order to bring more intelligence to edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems, for optimizing mobile edge computing, caching and communication. And thus, we design the "In-Edge AI" framework in order to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters for a better training and inference of the models, and thus to carry out dynamic system-level optimization and application-level enhancement while reducing the unnecessary system communication load. "In-Edge AI" is evaluated and proved to have near-optimal performance but relatively low overhead of learning, while the system is cognitive and adaptive to mobile communication systems. Finally, we discuss several related challenges and opportunities for unveili

Connected through hyperedge HE-1 (induced by source paper "energy efficient federated edge learning with joint communication and computation design"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "an overview of data importance aware radio resource management for edge machine learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "convergence time optimization for federated learning over wireless networks"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "federated neuromorphic learning of spiking neural networks for low power edge intelligence"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-3 (induced by source paper "a collaborative learning framework via federated meta learning"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "on the convergence theory of gradient based model agnostic meta learning algorithms"
    - "on first order meta learning algorithms"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "adversarial meta learning"
    - "federated meta learning for recommendation"
    - "generalizing to unseen domains via adversarial data augmentation"
    - "an overview of multi task learning in deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "application of machine learning in wireless networks key techniques and open issues"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "network slicing in fog radio access networks issues and challenges"
    - "communication efficient learning of deep networks from decentralized data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "focus dealing with label quality disparity in federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "zeno distributed stochastic gradient descent with suspicion based fault tolerance"
    - "federated learning with non iid data"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "ai oriented medical workload allocation for hierarchical cloud edge device computing"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "ai benchmark running deep neural networks on android smartphones"
    - "pruning convolutional neural networks for resource efficient inference"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-8 (induced by source paper "a joint learning and communications framework for federated learning over wireless networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient radio resource allocation for federated edge learning"
    - "fast uplink grant for noma a federated learning based approach"
    - "towards federated learning at scale system design"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "when machine learning meets big data a wireless communication perspective"
    - "multi hop federated private data augmentation with sample compression"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"

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)