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: "a survey on soft subspace clustering"
  Abstract: Subspace clustering (SC) is a promising technology involving clusters that are identified based on their association with subspaces in high-dimensional spaces. SC can be classified into hard subspace clustering (HSC) and soft subspace clustering (SSC). While HSC algorithms have been studied extensively and are well accepted by the scientific community, SSC algorithms are relatively new. However, as they are said to be more adaptable than their HSC counterparts, SSC algorithms have been attracting more attention in recent years. A comprehensive survey of existing SSC algorithms and recent developments in the field are presented in this paper. SSC algorithms have been systematically classified into three main categories: conventional SSC (CSSC), independent SSC (ISSC), and extended SSC (XSSC). The characteristics of these algorithms are highlighted and potential future developments in the area of SSC are discussed. Through a comprehensive review of SSC, this paper aims to provide readers with a clear profile of existing SSC methods and to foster the development of more effective clustering technologies and significant research in this area.

Connected through hyperedge HE-1 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-2 (induced by source paper "supervised enhanced soft subspace clustering sessc for tsk fuzzy classifiers"):
  with members:
    - "concise fuzzy system modeling integrating soft subspace clustering and sparse learning"

Connected through hyperedge HE-3 (induced by source paper "optimize tsk fuzzy systems for big data classification problems bag of tricks"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-4 (induced by source paper "concise fuzzy system modeling integrating soft subspace clustering and sparse learning"):
  with members:
    - "interpreting cnns via decision trees"
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "practical black box attacks against machine learning"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "understanding deep image representations by inverting them"

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)