human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "demystifying differentiable programming shift reset the penultimate backpropagator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a differential form pullback programming language for higher order reverse mode automatic differentiation")
  Member "subspace clustering by block diagonal representation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic k means clustering via nonlinear programming")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep subspace clustering networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gatcluster self supervised gaussian attention network for image clustering")
  Member "interpretable convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional occupancy networks")
  Member "constructing the l2 graph for robust subspace learning and subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")

Question: Please tell me which class the source paper 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)