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: "federated learning in mobile edge networks a comprehensive survey"
  Abstract: In recent years, mobile devices are equipped with increasingly advanced sensing and computing capabilities. Coupled with advancements in Deep Learning (DL), this opens up countless possibilities for meaningful applications. Traditional Machine Learning (ML) approaches require the data to be centralized in a cloud server. However, this results in critical issues related to unacceptable latency and communication inefficiency. To this end, Mobile Edge Computing (MEC) has been proposed to bring intelligence closer to the edge, where data is produced. However, conventional enabling technologies for ML at mobile edge networks still require personal data to be shared with external parties, e.g., edge servers. Recently, in light of increasingly stringent data privacy legislation and growing privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train an ML model required by the server. The end devices then send the model updates rather than raw data to the server for aggregation. FL can serve as an enabling technology in mobile edge networks since it enables the collaborative training of an ML model and also enables DL for mobile edge network optimization. However, in a large-scale and complex mobile edge network, heterogeneous devices with varying constraints are involved. This raises challenges of communication costs, resource allocation, and privacy and security in the implementation of FL at scale. In this survey, we begin with an introduction to the background and fundamentals of FL. Then, we highlight the aforementioned challenges of FL implementation and review existing solutions. Furthermore, we present the applications of FL for mobile edge network optimization. Finally, we discuss the important challenges, open issues and future research directions in FL.

Connected through hyperedge HE-1 (induced by source paper "ternary compression for communication efficient federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "model compression via distillation and quantization"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "multi objective evolutionary federated learning"
    - "federated learning with non iid data"

Connected through hyperedge HE-2 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art"):
  with members:
    - "elfish resource aware federated learning on heterogeneous edge devices"
    - "model pruning enables efficient federated learning on edge devices"
    - "one shot federated learning"
    - "privacy is what we care about experimental investigation of federated learning on edge devices"
    - "towards federated learning at scale system design"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-3 (induced by source paper "joint parameter and bandwidth allocation for improving the efficiency of partitioned edge learning"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "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"
    - "accelerating dnn training in wireless federated edge learning system"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-4 (induced by source paper "exploiting unlabeled data in smart cities using federated learning"):
  with members:
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "don t use large mini batches use local sgd"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-5 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey"):
  with members:
    - "ekiden a platform for confidentiality preserving trustworthy and performant smart contracts"
    - "a key 6g challenge and opportunity connecting the remaining 4 billions a survey on rural connectivity"
    - "on safeguarding privacy and security in the framework of federated learning"
    - "a survey on consensus mechanisms and mining strategy management in blockchain networks"
    - "trustsas a trustworthy spectrum access system for the 3 5 ghz cbrs band"
    - "blockchain enabled authentication handover with efficient privacy protection in sdn based 5g networks"
    - "6g wireless communication systems applications requirements technologies challenges and research directions"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"

Connected through hyperedge HE-6 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "resource allocation in mobility aware federated learning networks a deep reinforcement learning approach"):
  with members:
    - "efficient training management for mobile crowd machine learning a deep reinforcement learning approach"
    - "deep reinforcement learning for time scheduling in rf powered backscatter cognitive radio networks"
    - "a survey on mobile edge computing the communication perspective"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "deep reinforcement learning with double q learning"

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)