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: "massive scale online feature selection for sparse ultra high dimensional data"
  Abstract: Feature Selection is an important technique in machine learning and pattern classification, especially for handling high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world, especially when data arrives sequentially. Recent years have witnessed some emerging feature selection techniques using online learning. Despite enjoying significant advantages in efficiency and scalability, the existing online feature selection methods are not always accurate enough, and still not sufficiently fast when handling massive-scale data with ultra-high dimensionality. To address the limitations, we propose a novel online feature selection method by exploiting second-order information with optimized implementations, which not only improves the learning efficacy, but also significantly enhances computational efficiency. We conduct extensive experiments for evaluating both learning accuracy and time cost of different algorithms on massive-scale synthetic and real-world datasets, including a dataset with billion-scale features. Our results show that our technique achieves highly competitive accuracy as compared with state-of-the-art batch feature selection methods, but consumes significantly low computational cost that is orders of magnitude lower than both state-of-the-art batch and online feature selection methods. On a billion-scale synthetic dataset (1-billion dimensions, 1-billion nonzero features, and 1-million samples), our algorithm took only eight minutes with a normal single machine.

Connected through hyperedge HE-1 (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-2 (induced by source paper "moanofs multi objective automated negotiation based online feature selection system for big data classification"):
  with members:
    - "online feature selection with group structure analysis"
    - "large scale online feature selection for ultra high dimensional sparse data"
    - "sparse online learning via truncated gradient"

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 "relevant based structure learning for feature selection"):
  with members:
    - "a review of feature selection methods based on mutual information"
    - "towards ultrahigh dimensional feature selection for big data"

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)