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: "deep learning and quantum entanglement fundamental connections with implications to network design"
  Abstract: Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge embedded in the network architecture, is one of them. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning. We use this connection for asserting novel theoretical observations regarding the role that the number of channels in each layer of the convolutional network fulfills in the overall inductive bias. Specifically, we show an equivalence between the function realized by a deep convolutional arithmetic circuit (ConvAC) and a quantum many-body wave function, which relies on their common underlying tensorial structure. This facilitates the use of quantum entanglement measures as well-defined quantifiers of a deep network's expressive ability to model intricate correlation structures of its inputs. Most importantly, the construction of a deep ConvAC in terms of a Tensor Network is made available. This description enables us to carry a graph-theoretic analysis of a convolutional network, with which we demonstrate a direct control over the inductive bias of the deep network via its channel numbers, that are related to the min-cut in the underlying graph. This result is relevant to any practitioner designing a network for a specific task. We theoretically analyze ConvACs, and empirically validate our findings on more common ConvNets which involve ReLU activations and max pooling. Beyond the results described above, the description of a deep convolutional network in well-defined graph-theoretic tools and the formal connection to quantum entanglement, are two interdisciplinary bridges that are brought forth by this work.

Connected through hyperedge HE-1 (induced by source paper "a prior of a googol gaussians a tensor ring induced prior for generative models"):
  with members:
    - "gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images"
    - "spectral normalization for generative adversarial networks"
    - "latent dirichlet allocation in generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "deep voice 3 scaling text to speech with convolutional sequence learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "compressing recurrent neural network with tensor train"

Connected through hyperedge HE-2 (induced by source paper "a generalized language model in tensor space"):
  with members:
    - "a quantum many body wave function inspired language modeling approach"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "improving neural language models with a continuous cache"
    - "exploring the limits of language modeling"
    - "pointer sentinel mixture models"
    - "on multiplicative integration with recurrent neural networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "recurrent neural network regularization"

Connected through hyperedge HE-3 (induced by source paper "a quantum many body wave function inspired language modeling approach"):
  with members:
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "neural ranking models with weak supervision"
    - "attentive pooling networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "modeling relational information in question answer pairs with convolutional neural networks"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "character aware neural language models"
    - "convolutional neural network architectures for matching natural language sentences"

Connected through hyperedge HE-4 (induced by source paper "opening the black box of deep learning"):
  with members:
    - "decoupled networks"
    - "a mathematical framework for superintelligent machines"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "what do we understand about convolutional networks"
    - "large scale distributed neural network training through online distillation"
    - "deep learning a critical appraisal"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "mathematics of deep learning"

Connected through hyperedge HE-5 (induced by source paper "large field and high resolution detecting needle in haystack"):
  with members:
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "joint object and part segmentation using deep learned potentials"
    - "attention to scale scale aware semantic image segmentation"
    - "zoom better to see clearer human and object parsing with hierarchical auto zoom net"
    - "on the expressive power of deep learning a tensor analysis"
    - "computational role of eccentricity dependent cortical magnification"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-6 (induced by source paper "analysis and design of convolutional networks via hierarchical tensor decompositions"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "tensorial mixture models"
    - "boosting dilated convolutional networks with mixed tensor decompositions"
    - "densely connected convolutional networks"
    - "learning functions when is deep better than shallow"
    - "neural machine translation in linear time"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-7 (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"
    - "analysis and design of convolutional networks via hierarchical tensor decompositions"
    - "expressive power of recurrent neural networks"
    - "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)