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 spatio temporal pattern prediction for electric vehicle networks"):
  Abstract: Information about the spatio-temporal pattern of electricity energy carried by EVs, instead of EVs themselves, is crucial for EVs to establish more effective and intelligent interactions with the smart grid. In this paper, we propose a framework for predicting the amount of the electricity energy stored by a large number of EVs aggregated within different city-scale regions, based on spatio-temporal pattern of the electricity energy. The spatial pattern is modeled via using a neural network based spatial predictor, while the temporal pattern is captured via using a linear-chain conditional random field (CRF) based temporal predictor. Two predictors are fed with spatial and temporal features respectively, which are extracted based on real trajectories data recorded in Beijing. Furthermore, we combine both predictors to build the spatio-temporal predictor, by using an optimal combination coefficient which minimizes the normalized mean square error (NMSE) of the predictions. The prediction performance is evaluated based on extensive experiments covering both spatial and temporal predictions, and the improvement achieved by the combined spatio-temporal predictor. The experiment results show that the NMSE of the spatio-temporal predictor is maintained below 0.1 for all investigate regions of Beijing. We further visualize the prediction and discuss the potential benefits can be brought to smart grid scheduling and EV charging by utilizing the proposed framework.

Includes members:
  Member "convergence analysis of the information matrix in gaussian belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast resampling of three dimensional point clouds via graphs")
  Member "convergence analysis of belief propagation for pairwise linear gaussian models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed multi user wireless charging power allocation")
  Member "distributed convergence verification for gaussian belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed multi user wireless charging power allocation")
  Member "smart charging for electric vehicles a survey from the algorithmic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differential privacy techniques for cyber physical systems a survey")
  Member "distributed hybrid power state estimation under pmu sampling phase errors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rf based energy harvesting in decode and forward relaying systems ergodic and outage capacities")
  Member "distributed clock skew and offset estimation in wireless sensor networks asynchronous algorithm and convergence analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cooperative network synchronization asymptotic analysis")

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)