human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "backpropagation generalized for output derivatives"
  Abstract: Backpropagation algorithm is the cornerstone for neural network analysis. Paper extends it for training any derivatives of neural network's output with respect to its input. By the dint of it feedforward networks can be used to solve or verify solutions of partial or simple, linear or nonlinear differential equations. This method vastly differs from traditional ones like finite differences on a mesh. It contains no approximations, but rather an exact form of differential operators. Algorithm is built to train a feed forward network with any number of hidden layers and any kind of sufficiently smooth activation functions. It's presented in a form of matrix-vector products so highly parallel implementation is readily possible. First part derives the method for 2D case with first and second order derivatives, second part extends it to N-dimensional case with any derivatives. All necessary expressions for using this method to solve most applied PDE can be found in Appendix D.

Connected through 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"):
  with members:
    - "fast capsnet for lung cancer screening"
    - "brain tumor type classification via capsule networks"
    - "improved explainability of capsule networks relevance path by agreement"
    - "dynamic routing between capsules"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

Connected through hyperedge HE-2 (induced by source paper "enhancing approximation abilities of neural networks by training derivatives"):
  with members:
    - "efficient implementation of a higher order language with built in ad"
    - "all you need is a good init"
    - "batch normalized maxout network in network"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "neural networks catching up with finite differences in solving partial differential equations in higher dimensions"):
  with members:
    - "enhancing approximation abilities of neural networks by training derivatives"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)