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 "enhancing approximation abilities of neural networks by training derivatives"):
  Abstract: Method for increasing precision of feedforward networks is presented. With the aid of it they can serve as a better tool for describing smooth functions. Namely, it is shown that when training uses derivatives of target function up to the fourth order, approximation can be nearly machine precise. It is demonstrated in a number of cases: 2D function approximation, training autoencoder to compress 3D spiral into 1D, and solving 2D boundary value problem for Poisson equation with nonlinear source. In the first case cost function in addition to squared difference between output and target contains squared differences between their derivatives with respect to input variables. Training autoencoder is similar, but differentiation is done with respect to parameter that generates the spiral. Supplied with derivatives up to the fourth the method is found to be 30-200 times more accurate than regular training provided networks are of sufficient size and depth. Solving PDE is more practical since higher derivatives are not calculated beforehand, but information about them is extracted from the equation itself. Classical approach is to put perceptron in place of unknown function, choose the cost as squared residual and to minimize it with respect to weights. This would ensure that equation holds within some margin of error. Additional terms used in cost function are squared derivatives of the residual with respect to independent variables. Supplied with terms up to the second order the method is found to be 5 times more accurate. Efficient GPU version of algorithm is proposed.

Includes members:
  Member "backpropagation generalized for output derivatives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluation of neural networks for image recognition applications designing a 0 1 milp model of a cnn to create adversarials")
  Member "efficient implementation of a higher order language with built in ad":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demystifying differentiable programming shift reset the penultimate backpropagator")
  Member "all you need is a good init":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")
  Member "batch normalized maxout network in network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions")
  Member "tensorflow large scale machine learning on heterogeneous distributed systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "delving deep into rectifiers surpassing human level performance on imagenet classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.NE(Neural and Evolutionary Computing)