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 "machine learning at the network edge a survey"):
  Abstract: Devices comprising the Internet of Things, such as sensors and small cameras, usually have small memories and limited computational power. The proliferation of such resource-constrained devices in recent years has led to the generation of large quantities of data. These data-producing devices are appealing targets for machine learning applications but struggle to run machine learning algorithms due to their limited computing capability. They typically offload data to external computing systems (such as cloud servers) for further processing. The results of the machine learning computations are communicated back to the resource-scarce devices, but this worsens latency, leads to increased communication costs, and adds to privacy concerns. Therefore, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning has been deployed at the edge of computer networks.

Includes members:
  Member "energy efficient radio resource allocation for federated edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "network offloading policies for cloud robotics a learning based approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a fog robotics approach to deep robot learning application to object recognition and grasp planning in surface decluttering")
  Member "deep learning at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on parameter tuning in meta learning for computer vision")
  Member "edge intelligence paving the last mile of artificial intelligence with edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse optimization for green edge ai inference")
  Member "edgelens deep learning based object detection in integrated iot fog and cloud computing environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments")
  Member "dabnn a super fast inference framework for binary neural networks on arm devices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "forward and backward information retention for accurate binary neural networks")
  Member "pcamp performance comparison of machine learning packages on the edges":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing")
  Member "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")

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)