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: "first analysis of local gd on heterogeneous data"
  Abstract: We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise arbitrary functions. Problems of this form and local gradient descent as a solution method are of importance in federated learning, where each function is based on private data stored by a user on a mobile device, and the data of different users can be arbitrarily heterogeneous. We show that in a low accuracy regime, the method has the same communication complexity as gradient descent.

Connected through hyperedge HE-1 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"

Connected through hyperedge HE-2 (induced by source paper "adaptive federated optimization"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "federated learning challenges methods and future directions"
    - "on the convergence of adam and beyond"
    - "the non iid data quagmire of decentralized machine learning"

Connected through hyperedge HE-3 (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"
    - "client edge cloud hierarchical federated learning"
    - "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-4 (induced by source paper "gradient descent with compressed iterates"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust and communication efficient collaborative learning"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated machine learning concept and applications"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

Connected through hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for on device federated learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated learning of out of vocabulary words"
    - "federated learning for emoji prediction in a mobile keyboard"

Connected through hyperedge HE-6 (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"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "fair resource allocation in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-7 (induced by source paper "tighter theory for local sgd on identical and heterogeneous data"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "variance reduced local sgd with lower communication complexity"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "distributed optimization for over parameterized learning"
    - "on the convergence of local descent methods in federated learning"
    - "slowmo improving communication efficient distributed sgd with slow momentum"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "local adaalter communication efficient stochastic gradient descent with adaptive learning rates"

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)