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: "ldmnet low dimensional manifold regularized neural networks"
  Abstract: Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training set is very small. Data-dependent regularizations are mostly motivated by the observation that data of interest lie close to a manifold, which is typically hard to parametrize explicitly and often requires human input of tangent vectors. These methods typically only focus on the geometry of the input data, and do not necessarily encourage the networks to produce geometrically meaningful features. To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features. In LDMNet, we regularize the network by encouraging the combination of the input data and the output features to sample a collection of low dimensional manifolds, which are searched efficiently without explicit parametrization. To achieve this, we directly use the manifold dimension as a regularization term in a variational functional. The resulting Euler-Lagrange equation is a Laplace-Beltrami equation over a point cloud, which is solved by the point integral method without increasing the computational complexity. We demonstrate two benefits of LDMNet in the experiments. First, we show that LDMNet significantly outperforms widely-used network regularizers such as weight decay and DropOut. Second, we show that LDMNet can be designed to extract common features of an object imaged via different modalities, which proves to be very useful in real-world applications such as cross-spectral face recognition.

Connected through hyperedge HE-1 (induced by source paper "coopsubnet cooperating subnetwork for data driven regularization of deep networks under limited training budgets"):
  with members:
    - "microscopic nuclei classification segmentation and detection with improved deep convolutional neural network dcnn approaches"
    - "regularization for deep learning a taxonomy"
    - "deepssm a deep learning framework for statistical shape modeling from raw images"
    - "autoaugment learning augmentation policies from data"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "end to end representation learning for correlation filter based tracking"
    - "learning a predictable and generative vector representation for objects"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-2 (induced by source paper "optimizing nondecomposable data dependent regularizers via lagrangian reparameterization offers significant performance and efficiency gains"):
  with members:
    - "differentiable sorting using optimal transport the sinkhorn cdf and quantile operator"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "object detection with deep learning a review"
    - "convex constrained sparse additive modeling and its extensions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "automatic differentiation in machine learning a survey"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-3 (induced by source paper "cure curvature regularization for missing data recovery"):
  with members:
    - "weighted nonlocal total variation in image processing"
    - "manifold based low rank regularization for image restoration and semi supervised learning"

Connected through hyperedge HE-4 (induced by source paper "peernets exploiting peer wisdom against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep dictionary learning a parametric network approach"
    - "adversarial attacks and defences competition"
    - "dynamic graph cnn for learning on point clouds"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "non local neural networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-5 (induced by source paper "deep learning with data dependent implicit activation function"):
  with members:
    - "dual path networks"
    - "identity mappings in deep residual networks"
    - "identity matters in deep learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "stop memorizing a data dependent regularization framework for intrinsic pattern learning"):
  with members:
    - "understanding deep learning requires rethinking generalization"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"

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.CV(Computer Vision and Pattern Recognition)