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 "crop yield prediction using deep neural networks"):
  Abstract: The world’s population is on the rise and in order to feed the world in 2050, food production will need to increase by 70% [1]. As a result, it is of great importance to construct powerful predictive models for phenotype prediction based on Genotype and Environment data (so-called G by E problem). The objective of the G by E analysis is to understand how genotype and the environment jointly determine the phenotype (such as crop yield and disease resistance) of plant or animal species. In this research, deep neural networks are trained and used as predictive models. Deep neural networks have become a popular tool in supervise learning due to considerable ability in training nonlinear features [5]. Recent articles have stated that the network depth is a vital factor in decreasing classification or regression error. But, deeper networks have a so-called vanishing/exploding gradients problem which makes the training and optimizing deeper networks difficult. He et al. proposed residual learning method which alleviates this problem very well and showed that deep residual networks are significantly better and more efficient than previous typical networks [5]. As a result, residual training has been used in this research to prevent gradient degradation and ease the optimization process. Finally, since it is difficult to predict the yield difference directly, two separate residual neural networks have been trained to predict yield and check yield. After training the networks, the RMSE for check yield and yield are 8.23 and 10.52, respectively, which are very good because of considerable amount of missing values, uncertainty, and complexity in the datasets.

Includes members:
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  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 "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "striving for simplicity the all convolutional net":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")

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)