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: "on overcoming the curse of dimensionality in neural networks"
  Abstract: Let $A$ be a set, $V$ a Hilbert space. Let $H$ be a Hilbert space of functions $f:A\to V$ such that we have $\sup_{x\in A}\Vert f(x)\Vert_{V}\leq M \Vert f\Vert_H$. For $i=1,\cdots,n$, let $(x_i,y_i)\in A\times V$ comprise our dataset. Let $f^*\in H$ be the unique global minimizer of the functional \begin{equation*} u(f) = \frac{\lambda}{2}\Vert f\Vert_{H}^{2} +\frac{1}{2} \frac{1}{n}\sum_{i=1}^{n}\Vert f(x_i)-y_i\Vert_{V}^{2}. \end{equation*} #R##N#In this paper we show that for each $k\in\mathbb{N}$ there exists a two layer network where the first layer has $k$ number of basis functions $\Phi_{\epsilon_{i_j},x_{i_j}}$ for $i_1,\cdots,i_k\in\{1,\cdots,n\}$ and the second layer takes a weighted summation of the first layer, such that the functions $f_k$ realized by these networks satisfy \begin{equation*} E\left[ \Vert F_{k}-f^*\Vert_{H}^{2} \right] \leq \bigl(o(1) + \frac{C}{\lambda^2} (\frac{1}{\lambda}+M^2)u(f^*) \bigr) \frac{1}{k} . \end{equation*} #R##N#Thus the $x_i$ do not need to be in a linear space and $y_i$ are in a possibly infinite dimensional Hilbert space. The error rate is independent of the dimension of $V$ and data size $n$.

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)