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: "fair resource allocation in federated learning"
  Abstract: Federated learning involves training statistical models in massive, heterogeneous networks. Naively minimizing an aggregate loss function in such a network may disproportionately advantage or disadvantage some of the devices. In this work, we propose q-Fair Federated Learning (q-FFL), a novel optimization objective inspired by resource allocation in wireless networks that encourages a more fair (i.e., lower-variance) accuracy distribution across devices in federated networks. To solve q-FFL, we devise a communication-efficient method, q-FedAvg, that is suited to federated networks. We validate both the effectiveness of q-FFL and the efficiency of q-FedAvg on a suite of federated datasets, and show that q-FFL (along with q-FedAvg) outperforms existing baselines in terms of the resulting fairness, flexibility, and efficiency.

Connected through hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "advances and open problems in federated learning"
    - "how good is sgd with random shuffling"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"

Connected through hyperedge HE-2 (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-3 (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"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "a generic framework for privacy preserving deep learning"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of local descent methods in federated learning"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "one shot federated learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-5 (induced by source paper "federated user representation learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "pytorch biggraph a large scale graph embedding system"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "personalized language model for query auto completion"

Connected through hyperedge HE-6 (induced by source paper "toward an automated auction framework for wireless federated learning services market"):
  with members:
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "agnostic federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "joint service pricing and cooperative relay communication for federated learning"
    - "federated learning with non iid data"
    - "optimal auctions through deep learning"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-7 (induced by source paper "representation of federated learning via worst case robust optimization theory"):
  with members:
    - "federated machine learning concept and applications"
    - "federated optimization for heterogeneous networks"
    - "emnist an extension of mnist to handwritten letters"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-8 (induced by source paper "federated meta learning with fast convergence and efficient communication"):
  with members:
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "a closer look at few shot classification"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "deep meta learning learning to learn in the concept space"

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.LG(Machine Learning)