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 "short term load forecasting with dense average network"):
  Abstract: Short-trem Load forecasting is of great significance to power system. In this paper, we propose a new connection, Dense Average connection, in which the outputs of all previous layers are averaged as the input of the next layer in a feedforward method.Compared with fully connected layer, the Dense Average connection does not introduce new training parameters.Based on the Dense Average connection,we build the Dense Average Network for load forecasting. In two public datasets and one real dataset, we verify the validity of the model.Compared with ANN, our proposed model has better convergence and prediction effect.Meanwhile, we use the ensemble method to further improve the prediction effect. In order to verify the reliability of the model, we also disturb the input of the model to different degrees. Experimental results show that the proposed model is very robust.

Includes members:
  Member "catboost gradient boosting with categorical features support":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep differentiable forest with sparse attention for the tabular data")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "efficient object localization using convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proposal learning for semi supervised object detection")

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)