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 "wireless traffic prediction with scalable gaussian process framework algorithms and verification"):
  Abstract: The cloud radio access network (C-RAN) is a promising paradigm to meet the stringent requirements of the fifth generation (5G) wireless systems. Meanwhile, wireless traffic prediction is a key enabler for C-RANs to improve both the spectrum efficiency and energy efficiency through load-aware network managements. This paper proposes a scalable Gaussian process (GP) framework as a promising solution to achieve large-scale wireless traffic prediction in a cost-efficient manner. Our contribution is three-fold. First, to the best of our knowledge, this paper is the first to empower GP regression with the alternating direction method of multipliers (ADMM) for parallel hyper-parameter optimization in the training phase, where such a scalable training framework well balances the local estimation in baseband units (BBUs) and information consensus among BBUs in a principled way for large-scale executions. Second, in the prediction phase, we fuse local predictions obtained from the BBUs via a cross-validation based optimal strategy, which demonstrates itself to be reliable and robust for general regression tasks. Moreover, such a cross-validation based optimal fusion strategy is built upon a well acknowledged probabilistic model to retain the valuable closed-form GP inference properties. Third, we propose a C-RAN based scalable wireless prediction architecture, where the prediction accuracy and the time consumption can be balanced by tuning the number of the BBUs according to the real-time system demands. Experimental results show that our proposed scalable GP model can outperform the state-of-the-art approaches considerably, in terms of wireless traffic prediction performance.

Includes members:
  Member "on asymptotic properties of hyperparameter estimators for kernel based regularization methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on input design for regularized lti system identification power constrained input")
  Member "green heterogeneous cloud radio access networks potential techniques performance trade offs and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the age of information in internet of things systems with correlated devices")
  Member "recent advances in cloud radio access networks system architectures key techniques and open issues":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "electricity demand forecasting by multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "building energy load forecasting using deep neural networks")
  Member "an approach for spatial temporal traffic modeling in mobile cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on mobile edge networks convergence of computing caching and communications")
  Member "system architecture and key technologies for 5g heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "generalized product of experts for automatic and principled fusion of gaussian process predictions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "product kanerva machines factorized bayesian memory")

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)