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: "multiscale hierarchical convolutional networks"
  Abstract: Deep neural network algorithms are difficult to analyze because they lack structure allowing to understand the properties of underlying transforms and invariants. Multiscale hierarchical convolutional networks are structured deep convolutional networks where layers are indexed by progressively higher dimensional attributes, which are learned from training data. Each new layer is computed with multidimensional convolutions along spatial and attribute variables. We introduce an efficient implementation of such networks where the dimensionality is progressively reduced by averaging intermediate layers along attribute indices. Hierarchical networks are tested on CIFAR image data bases where they obtain comparable precisions to state of the art networks, with much fewer parameters. We study some properties of the attributes learned from these databases.

Connected through hyperedge HE-1 (induced by source paper "interpretable neuron structuring with graph spectral regularization"):
  with members:
    - "learning discrete structures for graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "towards a definition of disentangled representations"
    - "emergence of invariance and disentanglement in deep representations"
    - "dynamic routing between capsules"
    - "interpretable convolutional neural networks"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "teaching compositionality to cnns"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural networks via node varying graph filters"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "reconstruction of graph signals through percolation from seeding nodes"
    - "densely connected convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "sampling of graph signals with successive local aggregations"
    - "hierarchical overlapping clustering of network data using cut metrics"
    - "discrete signal processing on graphs sampling theory"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-3 (induced by source paper "scaling the scattering transform deep hybrid networks"):
  with members:
    - "deep unsupervised learning through spatial contrasting"
    - "building a regular decision boundary with deep networks"
    - "inverse problems with invariant multiscale statistics"
    - "what makes imagenet good for transfer learning"
    - "wide residual networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "multi scale dense networks for resource efficient image classification"):
  with members:
    - "adaptive neural networks for efficient inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "convolutional neural fabrics"
    - "adaptive computation time for recurrent neural networks"
    - "deep networks with stochastic depth"
    - "end to end learning for self driving cars"
    - "xception deep learning with depthwise separable convolutions"
    - "fractalnet ultra deep neural networks without residuals"

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.LG(Machine Learning)