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: "fast bounded online gradient descent algorithms for scalable kernel based online learning"
  Abstract: Kernel-based online learning has often shown state-of-the-art performance for many online learning tasks. It, however, suffers from a major shortcoming, that is, the unbounded number of support vectors, making it non-scalable and unsuitable for applications with large-scale datasets. In this work, we study the problem of bounded kernel-based online learning that aims to constrain the number of support vectors by a predefined budget. Although several algorithms have been proposed in literature, they are neither computationally efficient due to their intensive budget maintenance strategy nor effective due to the use of simple Perceptron algorithm. To overcome these limitations, we propose a framework for bounded kernel-based online learning based on an online gradient descent approach. We propose two efficient algorithms of bounded online gradient descent (BOGD) for scalable kernel-based online learning: (i) BOGD by maintaining support vectors using uniform sampling, and (ii) BOGD++ by maintaining support vectors using non-uniform sampling. We present theoretical analysis of regret bound for both algorithms, and found promising empirical performance in terms of both efficacy and efficiency by comparing them to several well-known algorithms for bounded kernel-based online learning on large-scale datasets.

Connected through hyperedge HE-1 (induced by source paper "temporal network representation learning"):
  with members:
    - "heterogeneous network motifs"
    - "node2bits compact time and attribute aware node representations for user stitching"
    - "dylink2vec effective feature representation for link prediction in dynamic networks"
    - "models for capturing temporal smoothness in evolving networks for learning latent representation of nodes"
    - "deep generative dual memory network for continual learning"
    - "attributed social network embedding"
    - "struc2vec learning node representations from structural identity"
    - "psdvec a toolbox for incremental and scalable word embedding"

Connected through hyperedge HE-2 (induced by source paper "online learning a comprehensive survey"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "a survey of multi view representation learning"
    - "provably optimal algorithms for generalized linear contextual bandits"
    - "online deep learning learning deep neural networks on the fly"
    - "an online hierarchical algorithm for extreme clustering"
    - "the scaling limit of high dimensional online independent component analysis"
    - "efficient second order online learning by sketching"
    - "a framework of sparse online learning and its applications"

Connected through hyperedge HE-3 (induced by source paper "malicious url detection using machine learning a survey"):
  with members:
    - "online learning a comprehensive survey"
    - "a deep learning approach to fast format agnostic detection of malicious web content"
    - "urlnet learning a url representation with deep learning for malicious url detection"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "online deep learning learning deep neural networks on the fly"
    - "gradient episodic memory for continual learning"
    - "identifying malicious web domains using machine learning techniques with online credibility and performance data"
    - "sol a library for scalable online learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "approximation vector machines for large scale online learning"):
  with members:
    - "gaussian processes for big data"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "perceptron like algorithms for online learning to rank"):
  with members:
    - "top rank optimization in linear time"
    - "one pass auc optimization"
    - "on the generalization ability of online learning algorithms for pairwise loss functions"

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)