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: "k meansnet when k means meets differentiable programming"
  Abstract: In this paper, we study two challenging problems. The first one is how to implement \textit{k}-means in the neural network, which enjoys efficient training based on the stochastic algorithm. The second one is how to enhance the interpretability of network design for clustering. To solve the problems, we propose a neural network which is a novel formulation of the vanilla $k$-means objective. Our contribution is in twofold. From the view of neural networks, the proposed \textit{k}-meansNet is with explicit interpretability in neural processing. We could understand not only why the network structure is presented like itself but also why it could perform data clustering. Such an interpretable neural network remarkably differs from the existing works that usually employ visualization technique to explain the result of the neural network. From the view of \textit{k}-means, three highly desired properties are achieved, i.e. robustness to initialization, the capability of handling new coming data, and provable convergence. Extensive experimental studies show that our method achieves promising performance comparing with 12 clustering methods on some challenging datasets.

Connected through hyperedge HE-1 (induced by source paper "softmax based classification is k means clustering formal proof consequences for adversarial attacks and improvement through centroid based tailoring"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "how deep is deep enough quantifying class separability in the hidden layers of deep neural networks"
    - "on evaluating adversarial robustness"
    - "impostor networks for fast fine grained recognition"
    - "mathematical analysis of adversarial attacks"
    - "learning latent representations in neural networks for clustering through pseudo supervision and graph based activity regularization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "how deep is deep enough optimizing deep neural network architecture"

Connected through hyperedge HE-2 (induced by source paper "differentiable linearized admm"):
  with members:
    - "lifted proximal operator machines"
    - "optimization algorithm inspired deep neural network structure design"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "deep image prior"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "unrolled optimization with deep priors"
    - "densely connected convolutional networks"

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)