human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "federated neuromorphic learning of spiking neural networks for low power edge intelligence"):
  Abstract: Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inference. On-device training is, however, constrained by the limited amount of data available at each device. In this paper, we propose to mitigate this problem via cooperative training through Federated Learning (FL). To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN. FL-SNN leverages local feedback signals within each SNN, in lieu of backpropagation, and global feedback through communication via a base station. The scheme demonstrates significant advantages over separate training and features a flexible trade-off between communication load and accuracy via the selective exchange of synaptic weights.

Includes members:
  Member "surrogate gradient learning in spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "robust and communication efficient federated learning from non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "supervised learning in spiking neural networks with phase change memory synapses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design")
  Member "energy and policy considerations for deep learning in nlp":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "adaptive federated learning in resource constrained edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "signsgd compressed optimisation for non convex problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "sparse communication for distributed gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "supervised learning in spiking neural networks for precise temporal encoding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")

Question: Please tell me which class the source paper 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)