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 "energy efficient federated edge learning with joint communication and computation design"):
  Abstract: This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML parameters aggregation and the computation resource allocation for locally updating MLparameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML parameters to edge server, based on the non orthogonal multiple access and time division multiple access, respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit frequencies for local update. We propose efficient algorithms to optimally solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly improves the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.

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 "machine learning at the wireless edge distributed stochastic gradient descent over the air":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless federated learning with local differential privacy")
  Member "energy efficient federated learning over wireless communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems")
  Member "an overview of data importance aware radio resource management for edge machine learning":
    also connected through other hyperedges:
      (none)
  Member "in edge ai intelligentizing mobile edge computing caching and communication by federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "robust and communication efficient federated learning from non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "edge ai on demand accelerating deep neural network inference via edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse optimization for green edge ai inference")

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.IT(Information Theory)