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: "an adaptive locally connected neuron model focusing neuron"
  Abstract: We present a new artificial neuron model capable of learning its receptive field in the spatial domain of inputs. The name for the new model is focusing neuron because it can adapt both its receptive field location and size (aperture) during training. A network or a layer formed of such neurons can learn and generate unique connection structures for particular inputs/problems. The new model requires neither heuristics nor additional optimizations. Hence, all parameters, including those controlling the focus could be trained using the stochastic gradient descent optimization. We have empirically shown the capacity and viability of the new model with tests on synthetic and real datasets. We have constructed simple networks with one or two hidden layers; also employed fully connected networks with the same configurations as controls. In noise-added synthetic Gaussian blob datasets, we observed that focusing neurons can steer their receptive fields away from the redundant inputs and focused into more informative ones. Tests on common datasets such as MNIST have shown that a network of two hidden focusing layers can perform better (99.21% test accuracy) than a fully connected dense network with the same configuration.

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)