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: "client selection for federated learning with heterogeneous resources in mobile edge"
  Abstract: We envision a mobile edge computing (MEC) framework for machine learning (ML) technologies, which leverages distributed client data and computation resources for training high-performance ML models while preserving client privacy. Toward this future goal, this work aims to extend Federated Learning (FL), a decentralized learning framework that enables privacy-preserving training of models, to work with heterogeneous clients in a practical cellular network. The FL protocol iteratively asks random clients to download a trainable model from a server, update it with own data, and upload the updated model to the server, while asking the server to aggregate multiple client updates to further improve the model. While clients in this protocol are free from disclosing own private data, the overall training process can become inefficient when some clients are with limited computational resources (i.e., requiring longer update time) or under poor wireless channel conditions (longer upload time). Our new FL protocol, which we refer to as FedCS, mitigates this problem and performs FL efficiently while actively managing clients based on their resource conditions. Specifically, FedCS solves a client selection problem with resource constraints, which allows the server to aggregate as many client updates as possible and to accelerate performance improvement in ML models. We conducted an experimental evaluation using publicly-available large-scale image datasets to train deep neural networks on MEC environment simulations. The experimental results show that FedCS is able to complete its training process in a significantly shorter time compared to the original FL protocol.

Connected through hyperedge HE-1 (induced by source paper "tifl a tier based federated learning system"):
  with members:
    - "advances and open problems in federated learning"
    - "robust federated learning in a heterogeneous environment"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "client edge cloud hierarchical federated learning"
    - "federated machine learning concept and applications"
    - "edge assisted hierarchical federated learning with non iid data"
    - "applied federated learning improving google keyboard query suggestions"

Connected through hyperedge HE-2 (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"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"

Connected through hyperedge HE-3 (induced by source paper "update aware device scheduling for federated learning at the wireless edge"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "accelerating dnn training in wireless federated edge learning system"
    - "federated learning over wireless fading channels"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "age based scheduling policy for federated learning in mobile edge networks"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "joint status sampling and updating for minimizing age of information in the internet of things"

Connected through hyperedge HE-5 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "accelerating dnn training in wireless federated edge learning system"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "client edge cloud hierarchical federated learning"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "hybrid fl cooperative learning mechanism using non iid data in wireless networks"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-6 (induced by source paper "privacy preserving traffic flow prediction a federated learning approach"):
  with members:
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "towards federated learning at scale system design"
    - "reliable federated learning for mobile networks"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "fair resource allocation in federated learning"
    - "a generic framework for privacy preserving deep learning"

Connected through hyperedge HE-7 (induced by source paper "prophet proactive candidate selection for federated learning by predicting the qualities of training and reporting phases"):
  with members:
    - "towards federated learning at scale system design"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "federated learning for mobile keyboard prediction"
    - "communication efficient learning of deep networks from decentralized data"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "network density controlled decentralized parallel stochastic gradient descent in wireless systems"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "communication efficient learning of deep networks from decentralized data"
    - "very deep convolutional networks for large scale image recognition"

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)