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: "functional multi layer perceptron a non linear tool for functional data analysis"
  Abstract: In this paper, we study a natural extension of multi-layer perceptrons (MLP) to functional inputs. We show that fundamental results for classical MLP can be extended to functional MLP. We obtain universal approximation results that show the expressive power of functional MLP is comparable to that of numerical MLP. We obtain consistency results, which imply that the estimation of optimal parameters for functional MLP is statistically well defined. We finally show on simulated and real world data that the proposed model performs in a very satisfactory way.

Connected through hyperedge HE-1 (induced by source paper "deeponet learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"):
  with members:
    - "gmls nets a framework for learning from unstructured data"
    - "neural jump stochastic differential equations"
    - "deepxde a deep learning library for solving differential equations"
    - "attention is all you need"
    - "dynamic routing between capsules"

Connected through hyperedge HE-2 (induced by source paper "remaining useful life estimation using functional data analysis"):
  with members:
    - "two birds with one network unifying failure event prediction and time to failure modeling"
    - "accelerating deep learning with shrinkage and recall"

Connected through hyperedge HE-3 (induced by source paper "neural networks for complex data"):
  with members:
    - "une adaptation des cartes auto organisatrices pour des donn ees d ecrites par un tableau de dissimilarit es"
    - "theoretical properties of projection based multilayer perceptrons with functional inputs"
    - "fast algorithm and implementation of dissimilarity self organizing maps"

Connected through hyperedge HE-4 (induced by source paper "multi layer perceptrons and symbolic data"):
  with members:
    - "representation of functional data in neural networks"

Connected through hyperedge HE-5 (induced by source paper "theoretical properties of projection based multilayer perceptrons with functional inputs"):
  with members:
    - "representation of functional data in neural networks"

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)