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: "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
  Abstract: Much has been learned about plasticity of biological synapses from empirical studies. Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons. Synapses that converge onto the same neuron often behave as if they compete for a fixed resource; some survive the competition while others are eliminated. To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model. The game formalizes the intuition that Hebbian excitation tries to maximize correlations of neurons with their inputs, while anti-Hebbian inhibition tries to decorrelate neurons from each other. We further include a model of synaptic competition, which enables a neuron to eliminate all connections except those from its most strongly correlated inputs. Through empirical studies, we show that this facilitates the learning of sensory features that resemble parts of objects.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning by competing hidden units"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "biologically motivated algorithms for propagating local target representations"
    - "dense associative memory is robust to adversarial inputs"
    - "conducting credit assignment by aligning local representations"
    - "dense associative memory for pattern recognition"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "semi supervised learning with ladder networks"
    - "difference target propagation"

Connected through hyperedge HE-2 (induced by source paper "local unsupervised learning for image analysis"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "dense associative memory is robust to adversarial inputs"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "adversarial feature learning"
    - "dense associative memory for pattern recognition"
    - "deep residual learning for image recognition"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-3 (induced by source paper "a spiking neural network with local learning rules derived from nonnegative similarity matching"):
  with members:
    - "dictionary learning by dynamical neural networks"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "simple efficient and neural algorithms for sparse coding"
    - "a network of spiking neurons for computing sparse representations in an energy efficient way"

Connected through hyperedge HE-4 (induced by source paper "dictionary learning by dynamical neural networks"):
  with members:
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "how important is weight symmetry in backpropagation"
    - "sparse modeling for image and vision processing"
    - "non negative matrix factorization with sparseness constraints"

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)