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: "analysis and design of convolutional networks via hierarchical tensor decompositions"
  Abstract: The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power. Despite its wide acceptance and vast empirical evidence, formal analyses supporting this belief are scarce. The primary notions for formally reasoning about expressiveness are efficiency and inductive bias. Expressive efficiency refers to the ability of a network architecture to realize functions that require an alternative architecture to be much larger. Inductive bias refers to the prioritization of some functions over others given prior knowledge regarding a task at hand. In this paper we overview a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various convolutional network architectural features (depth, width, strides and more). The results presented shed light on the demonstrated effectiveness of convolutional networks, and in addition, provide new tools for network design.

Connected through hyperedge HE-1 (induced by source paper "on the optimization of deep networks implicit acceleration by overparameterization"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "depth separation for neural networks"
    - "on the ability of neural nets to express distributions"
    - "spurious local minima are common in two layer relu neural networks"
    - "training input output recurrent neural networks through spectral methods"
    - "identity matters in deep learning"
    - "the power of depth for feedforward neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "universal approximations of invariant maps by neural networks"):
  with members:
    - "polar transformer networks"
    - "rotation equivariant vector field networks"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "warped convolutions efficient invariance to spatial transformations"
    - "group equivariant convolutional networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "deep residual learning for image recognition"
    - "on invariance and selectivity in representation learning"

Connected through hyperedge HE-3 (induced by source paper "deep compression of sum product networks on tensor networks"):
  with members:
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "expressive power of recurrent neural networks"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "parallelized tensor train learning of polynomial classifiers"
    - "on the latent variable interpretation in sum product networks"
    - "tensor network alternating linear scheme for mimo volterra system identification"
    - "learning both weights and connections for efficient neural networks"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

Connected through hyperedge HE-4 (induced by source paper "provably efficient neural network representation for image classification"):
  with members:
    - "entanglement entropy of target functions for image classification and convolutional neural network"
    - "expressive power of recurrent neural networks"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "tensorizing neural networks"
    - "on the expressive power of deep learning a tensor analysis"

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)