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: "discontinuous piecewise polynomial neural networks"
  Abstract: An artificial neural network is presented based on the idea of connections between units that are only active for a specific range of input values and zero outside that range (and so are not evaluated outside the active range). The connection function is represented by a polynomial with compact support. The finite range of activation allows for great activation sparsity in the network and means that theoretically you are able to add computational power to the network without increasing the computational time required to evaluate the network for a given input. The polynomial order ranges from first to fifth order. Unit dropout is used for regularization and a parameter free weight update is used. Better performance is obtained by moving from piecewise linear connections to piecewise quadratic, even better performance can be obtained by moving to higher order polynomials. The algorithm is tested on the MAGIC Gamma ray data set as well as the MNIST data set.

Connected through hyperedge HE-1 (induced by source paper "variations on the chebyshev lagrange activation function"):
  with members:
    - "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "polynomial regression as an alternative to neural nets"
    - "semi supervised classification by reaching consensus among modalities"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "deepconsensus using the consensus of features from multiple layers to attain robust image classification"
    - "on the importance of normative data in speech based assessment"
    - "shake shake regularization"

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)