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: "wireless federated distillation for distributed edge learning with heterogeneous data"
  Abstract: Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.

Connected through hyperedge HE-1 (induced by source paper "personalized federated learning for intelligent iot applications a cloud edge based framework"):
  with members:
    - "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"
    - "fedhealth a federated transfer learning framework for wearable healthcare"
    - "federated learning with personalization layers"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "improving federated learning personalization via model agnostic meta learning"
    - "federated learning for healthcare informatics"
    - "federated learning challenges methods and future directions"
    - "edge ai on demand accelerating deep neural network inference via edge computing"

Connected through hyperedge HE-2 (induced by source paper "learning from peers at the wireless edge"):
  with members:
    - "decentralized federated learning a segmented gossip approach"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "a federated learning approach for mobile packet classification"
    - "peer to peer federated learning on graphs"
    - "agnostic federated learning"
    - "scheduling policies for federated learning in wireless networks"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "on the communication latency of wireless decentralized learning"):
  with members:
    - "update aware device scheduling for federated learning at the wireless edge"
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "robust and communication efficient collaborative learning"
    - "scheduling policies for federated learning in wireless networks"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-4 (induced by source paper "decentralized federated learning via sgd over wireless d2d networks"):
  with members:
    - "distributed learning in the non convex world from batch to streaming data and beyond"
    - "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "advances and open problems in federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "an introduction to decentralized stochastic optimization with gradient tracking"
    - "federated learning over wireless fading channels"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-5 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-6 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "communication efficient massive uav online path control federated learning meets mean field game theory"
    - "predictive control and communication co design a gaussian process regression approach"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "massive autonomous uav path planning a neural network based mean field game theoretic approach"
    - "accelerating federated learning via momentum gradient descent"
    - "advances and open problems in federated learning"
    - "gadmm fast and communication efficient framework for distributed machine learning"

Connected through hyperedge HE-7 (induced by source paper "l fgadmm layer wise federated group admm for communication efficient decentralized deep learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "accelerating federated learning via momentum gradient descent"
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "robust coreset construction for distributed machine learning"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "communication efficient distributed learning via lazily aggregated quantized gradients"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-8 (induced by source paper "energy efficient federated learning over wireless communication networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient radio resource allocation for federated edge learning"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "multi hop federated private data augmentation with sample compression"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "machine learning in the air"

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.IT(Information Theory)