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: "dense associative memory for pattern recognition"
  Abstract: A model of associative memory is studied, which stores and reliably retrieves many more patterns than the number of neurons in the network. We propose a simple duality between this dense associative memory and neural networks commonly used in deep learning. On the associative memory side of this duality, a family of models that smoothly interpolates between two limiting cases can be constructed. One limit is referred to as the feature-matching mode of pattern recognition, and the other one as the prototype regime. On the deep learning side of the duality, this family corresponds to feedforward neural networks with one hidden layer and various activation functions, which transmit the activities of the visible neurons to the hidden layer. This family of activation functions includes logistics, rectified linear units, and rectified polynomials of higher degrees. The proposed duality makes it possible to apply energy-based intuition from associative memory to analyze computational properties of neural networks with unusual activation functions - the higher rectified polynomials which until now have not been used in deep learning. The utility of the dense memories is illustrated for two test cases: the logical gate XOR and the recognition of handwritten digits from the MNIST data set.

Connected through hyperedge HE-1 (induced by source paper "hypercomplex valued recurrent correlation neural networks"):
  with members:
    - "an introduction to quaternion valued recurrent projection neural networks"
    - "quaternion convolutional neural networks for heterogeneous image processing"

Connected through hyperedge HE-2 (induced by source paper "quaternion valued recurrent projection neural networks on unit quaternions"):
  with members:
    - "an introduction to quaternion valued recurrent projection neural networks"

Connected through hyperedge HE-3 (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"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "semi supervised learning with ladder networks"
    - "difference target propagation"

Connected through hyperedge HE-4 (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"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "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"
    - "deep residual learning for image recognition"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-5 (induced by source paper "convolutional bipartite attractor networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "learning attractor dynamics for generative memory"
    - "reviving and improving recurrent back propagation"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "density estimation using real nvp"
    - "deeply recursive convolutional network for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank"):
  with members:
    - "dense associative memory is robust to adversarial inputs"
    - "dynamic routing between capsules"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-7 (induced by source paper "dense associative memory is robust to adversarial inputs"):
  with members:
    - "practical black box attacks against machine learning"
    - "adversary resistant deep neural networks with an application to malware detection"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "adversarial examples in the physical world"
    - "learning with a strong adversary"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"
    - "towards deep neural network architectures robust to adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-8 (induced by source paper "learning to update auto associative memory in recurrent neural networks for improving sequence memorization"):
  with members:
    - "end to end answer chunk extraction and ranking for reading comprehension"
    - "deep reinforcement learning for dialogue generation"
    - "squad 100 000 questions for machine comprehension of text"
    - "recurrent highway networks"
    - "neural architecture search with reinforcement learning"
    - "sequence to sequence rnns for text summarization"
    - "show attend and tell neural image caption generation with visual attention"
    - "learning dynamic boltzmann machines with spike timing dependent plasticity"

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)