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 intelligence at the edge with learning centric power allocation"):
  Abstract: While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computation power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective to radio resource allocation in learning driven scenarios. By employing an empirical classification error model that is supported by learning theory, the LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved by majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, to enable LCPA in large-scale settings, two optimization algorithms, termed mirror-prox LCPA and accelerated LCPA, are further proposed. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale algorithms reduce the computation time by orders of magnitude compared with MM-based LCPA but still achieve competing learning performance.

Includes members:
  Member "edge intelligence paving the last mile of artificial intelligence with edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference")
  Member "a vision of 6g wireless systems applications trends technologies and open research problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "a speculative study on 6g":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint power allocation and load balancing optimization for energy efficient cell free massive mimo networks")
  Member "the roadmap to 6g ai empowered wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "machine learning in the air":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "adaptive federated learning in resource constrained edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "deep learning in mobile and wireless networking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "patdnn achieving real time dnn execution on mobile devices with pattern based weight pruning")

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)