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: "matcha speeding up decentralized sgd via matching decomposition sampling"
  Abstract: The trade-off between convergence error and communication delays in decentralized stochastic gradient descent~(SGD) is dictated by the sparsity of the inter-worker communication graph. In this paper, we propose MATCHA, a decentralized SGD method where we use matching decomposition sampling of the base graph to parallelize inter-worker information exchange so as to significantly reduce communication delay. At the same time, under standard assumptions for any general topology, in spite of the significant reduction of the communication delay, MATCHA maintains the same convergence rate as that of the state-of-the-art in terms of epochs. Experiments on a suite of datasets and deep neural networks validate the theoretical analysis and demonstrate the effectiveness of the proposed scheme as far as reducing communication delays is concerned.

Connected through hyperedge HE-1 (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"
    - "robust and communication efficient collaborative learning"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "scheduling policies for federated learning in wireless networks"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-2 (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"
    - "an introduction to decentralized stochastic optimization with gradient tracking"
    - "federated learning over wireless fading channels"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-3 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "one shot federated learning"
    - "robust and communication efficient collaborative learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"
    - "an exact quantized decentralized gradient descent algorithm"

Connected through hyperedge HE-4 (induced by source paper "slowmo improving communication efficient distributed sgd with slow momentum"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "understanding the role of momentum in stochastic gradient methods"
    - "lookahead optimizer k steps forward 1 step back"
    - "roberta a robustly optimized bert pretraining approach"
    - "decentralized deep learning with arbitrary communication compression"
    - "signsgd with majority vote is communication efficient and fault tolerant"

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)