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: "orthogonal deep neural networks"
  Abstract: In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization. To this end, we first prove that DNNs are of local isometry on data distributions of practical interest; by using a new covering of the sample space and introducing the local isometry property of DNNs into generalization analysis, we establish a new generalization error bound that is both scale- and range-sensitive to singular value spectrum of each of networks' weight matrices. We prove that the optimal bound w.r.t. the degree of isometry is attained when each weight matrix has a spectrum of equal singular values, among which orthogonal weight matrix or a non-square one with orthonormal rows or columns is the most straightforward choice, suggesting the algorithms of OrthDNNs. We present both algorithms of strict and approximate OrthDNNs, and for the later ones we propose a simple yet effective algorithm called Singular Value Bounding (SVB), which performs as well as strict OrthDNNs, but at a much lower computational cost. We also propose Bounded Batch Normalization (BBN) to make compatible use of batch normalization with OrthDNNs. We conduct extensive comparative studies by using modern architectures on benchmark image classification. Experiments show the efficacy of OrthDNNs.

Connected through hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "beyond sharing weights for deep domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"
    - "learning to transfer examples for partial domain adaptation"
    - "open set domain adaptation for image and action recognition"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation via structurally regularized deep clustering"):
  with members:
    - "unsupervised multi class domain adaptation theory algorithms and practice"
    - "discriminative adversarial domain adaptation"
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "unsupervised domain adaptation via regularized conditional alignment"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "domain specific batch normalization for unsupervised domain adaptation"
    - "domain symmetric networks for adversarial domain adaptation"
    - "bridging theory and algorithm for domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "improving semantic analysis on point clouds via auxiliary supervision of local geometric priors"):
  with members:
    - "geonet deep geodesic networks for point cloud analysis"
    - "geometry aware generation of adversarial and cooperative point clouds"
    - "learning graph embedding with adversarial training methods"
    - "revisiting self supervised visual representation learning"
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-4 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  with members:
    - "unsupervised deep feature transfer for low resolution image classification"
    - "adversarially approximated autoencoder for image generation and manipulation"
    - "mdfn multi scale deep feature learning network for object detection"
    - "towards learning affine invariant representations via data efficient cnns"
    - "boosting occluded image classification via subspace decomposition based estimation of deep features"
    - "interpretable convolutional neural networks via feedforward design"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "regularization for deep learning a taxonomy"

Connected through hyperedge HE-5 (induced by source paper "convolutional spectral kernel learning"):
  with members:
    - "automated spectral kernel learning"
    - "functional variational bayesian neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "pytorch an imperative style high performance deep learning library"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "differentiable compositional kernel learning for gaussian processes"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "universality of deep convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "blessing of dimensionality mathematical foundations of the statistical physics of data"
    - "thinet a filter level pruning method for deep neural network compression"
    - "learning both weights and connections for efficient neural networks"
    - "shapenet an information rich 3d model repository"
    - "on the number of response regions of deep feed forward networks with piece wise linear activations"

Connected through hyperedge HE-7 (induced by source paper "approximated orthonormal normalisation in training neural networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "group normalization"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "decorrelated batch normalization"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

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)