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 "hp vpinns variational physics informed neural networks with domain decomposition"):
  Abstract: We formulate a general framework for hp-variational physics-informed neural networks (hp-VPINNs) based on the nonlinear approximation of shallow and deep neural networks and hp-refinement via domain decomposition and projection onto space of high-order polynomials. The trial space is the space of neural network, which is defined globally over the whole computational domain, while the test space contains the piecewise polynomials. Specifically in this study, the hp-refinement corresponds to a global approximation with local learning algorithm that can efficiently localize the network parameter optimization. We demonstrate the advantages of hp-VPINNs in accuracy and training cost for several numerical examples of function approximation and solving differential equations.

Includes members:
  Member "understanding and mitigating gradient pathologies in physics informed neural networks":
    also connected through other hyperedges:
      (none)
  Member "variational physics informed neural networks for solving partial differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding and mitigating gradient pathologies in physics informed neural networks")
  Member "nonlinear approximation and deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonparametric regression on low dimensional manifolds using deep relu networks")
  Member "varnet variational neural networks for the solution of partial differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for robotic mass transport cloaking")
  Member "function approximation by deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "d3m a deep domain decomposition method for partial differential equations":
    also connected through other hyperedges:
      (none)
  Member "locally adaptive activation functions with slope recovery term for deep and physics informed neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepxde a deep learning library for solving differential equations")
  Member "deepxde a deep learning library for solving differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deeponet learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators")

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)