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: "dxnn platform the shedding of biological inefficiencies"
  Abstract: This paper introduces a novel type of memetic algorithm based Topology and Weight Evolving Artificial Neural Network (TWEANN) system called DX Neural Network (DXNN). DXNN implements a number of interesting features, amongst which is: a simple and database friendly tuple based encoding method, a 2 phase neuroevolutionary approach aimed at removing the need for speciation due to its intrinsic population diversification effects, a new "Targeted Tuning Phase" aimed at dealing with "the curse of dimensionality", and a new Random Intensity Mutation (RIM) method that removes the need for crossover algorithms. The paper will discuss DXNN's architecture, mutation operators, and its built in feature selection method that allows for the evolved systems to expand and incorporate new sensors and actuators. I then compare DXNN to other state of the art TWEANNs on the standard double pole balancing benchmark, and demonstrate its superior ability to evolve highly compact solutions faster than its competitors. Then a set of oblation experiments is performed to demonstrate how each feature of DXNN effects its performance, followed by a set of experiments which demonstrate the platform's ability to create NN populations with exceptionally high diversity profiles. Finally, DXNN is used to evolve artificial robots in a set of two dimensional open-ended food gathering and predator-prey simulations, demonstrating the system's ability to produce ever more complex Neural Networks, and the system's applicability to the domain of robotics, artificial life, and coevolution.

Connected through hyperedge HE-1 (induced by source paper "genesys enabling continuous learning through neural network evolution in hardware"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "scaling deep learning on gpu and knights landing clusters"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "nmode neuro module evolution"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "eie efficient inference engine on compressed deep neural network"

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)