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: "deep toc a new method for estimating the solutions of pdes"
  Abstract: This article presents a new methodology called deep ToC that estimates the solutions of partial differential equations (PDEs) by combining neural networks with the Theory of Connections (ToC). ToC is used to transform PDEs with boundary conditions into unconstrained optimization problems by embedding the boundary conditions into a "constrained expression" that contains a neural network. The loss function for the unconstrained optimization problem is taken to be the square of the residual of the PDE. Then, the neural network is trained in an unsupervised manner to solve the unconstrained optimization problem. This methodology has two major advantages over other popular methods used to estimate the solutions of PDEs. First, this methodology does not need to discretize the domain into a grid, which becomes prohibitive as the dimensionality of the PDE increases. Instead, this methodology randomly samples points from the domain during the training phase. Second, after training, this methodology represents a closed form, analytical, differentiable approximation of the solution throughout the entire training domain. In contrast, other popular methods require interpolation if the estimated solution is desired at points that do not lie on the discretized grid. The deep ToC method for estimating the solution of PDEs is demonstrated on four problems with a variety of boundary conditions.

Connected through hyperedge HE-1 (induced by source paper "graph element networks adaptive structured computation and memory"):
  with members:
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "attentive neural processes"
    - "a comprehensive survey on graph neural networks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "graph neural networks a review of methods and applications"
    - "modular meta learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"

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.NA(Numerical Analysis)