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 edge cloud hierarchical federated learning"
  Abstract: Federated Learning is a collaborative machine learning framework to train a deep learning model without accessing clients' private data. Previous works assume one central parameter server either at the cloud or at the edge. The cloud server can access more data but with excessive communication overhead and long latency, while the edge server enjoys more efficient communications with the clients. To combine their advantages, we propose a client-edge-cloud hierarchical Federated Learning system, supported with a HierFAVG algorithm that allows multiple edge servers to perform partial model aggregation. In this way, the model can be trained faster and better communication-computation trade-offs can be achieved. Convergence analysis is provided for HierFAVG and the effects of key parameters are also investigated, which lead to qualitative design guidelines. Empirical experiments verify the analysis and demonstrate the benefits of this hierarchical architecture in different data distribution scenarios. Particularly, it is shown that by introducing the intermediate edge servers, the model training time and the energy consumption of the end devices can be simultaneously reduced compared to cloud-based Federated Learning.

Connected through hyperedge HE-1 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "central server free federated learning over single sided trust social networks"
    - "towards federated learning at scale system design"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-2 (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"
    - "federated machine learning concept and applications"
    - "edge assisted hierarchical federated learning with non iid data"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "applied federated learning improving google keyboard query suggestions"

Connected through hyperedge HE-3 (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"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "hybrid fl cooperative learning mechanism using non iid data in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-4 (induced by source paper "dynamic federated learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "variational federated multi task learning"
    - "asynchronous federated optimization"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "mobile edge intelligence and computing for the internet of vehicles"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "a mobility aware vehicular caching scheme in content centric networks model and optimization"
    - "edge assisted hierarchical federated learning with non iid data"
    - "cavbench a benchmark suite for connected and autonomous vehicles"
    - "federated learning strategies for improving communication efficiency"
    - "exploiting mobility in cache assisted d2d networks performance analysis and optimization"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "follow me at the edge mobility aware dynamic service placement for mobile edge computing"

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)