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 "an introduction to communication efficient edge machine learning"):
  Abstract: In the near future, Internet-of-Things (IoT) is expected to connect billions of devices (e.g., smartphones and sensors), which generate massive real-time data at the network edge. Intelligence can be distilled from the data to support next-generation AI-powered applications, which is called edge machine learning. One challenge faced by edge learning is the communication bottleneck, which is caused by the transmission of high-dimensional data from many edge devices to edge servers for learning. Traditional wireless techniques focusing only on efficient radio access are ineffective in tackling the challenge. Solutions should be based on a new approach that seamlessly integrates communication and computation. This has led to the emergence of a new cross-disciplinary paradigm called communication efficient edge learning. The main theme in the area is to design new communication techniques and protocols for efficient implementation of different distributed learning frameworks (i.e., federated learning) in wireless networks. This article provides an overview of the emerging area by introducing new design principles, discussing promising research opportunities, and providing design examples based on recent work.

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 "data importance aware user scheduling for communication efficient edge machine learning":
    also connected through other hyperedges:
      (none)
  Member "high dimensional stochastic gradient quantization for communication efficient edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "age based scheduling policy for federated learning in mobile edge networks")
  Member "deep joint source channel coding for wireless image transmission":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep joint transmission recognition for power constrained iot devices")
  Member "reduced dimension design of mimo over the air computing for data aggregation in clustered iot networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis")
  Member "towards an intelligent edge wireless communication meets machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "automatic recognition of space time constellations by learning on the grassmann manifold":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast analog transmission for high mobility wireless data acquisition in edge learning")
  Member "deep gradient compression reducing the communication bandwidth for distributed training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparse weight activation training")

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)