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: "online unsupervised multi view feature selection"
  Abstract: In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods.

Connected through hyperedge HE-1 (induced by source paper "a geometric approach to online streaming feature selection"):
  with members:
    - "geometric estimation of multivariate dependency"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "cross modal zero shot hashing"):
  with members:
    - "ranking based deep cross modal hashing"
    - "attribute guided network for cross modal zero shot hashing"
    - "from zero shot learning to conventional supervised classification unseen visual data synthesis"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "a survey on learning to hash"
    - "deep cross modal hashing"
    - "learning to hash for indexing big data a survey"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "ranking based deep cross modal hashing"):
  with members:
    - "a survey on learning to hash"
    - "deep cross modal hashing"
    - "deep semantic ranking based hashing for multi label image retrieval"
    - "simultaneous feature learning and hash coding with deep neural networks"
    - "learning to hash for indexing big data a survey"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-4 (induced by source paper "geometric online adaptation graph based osfs for streaming samples"):
  with members:
    - "geometric estimation of multivariate dependency"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "joint embedding of meta path and meta graph for heterogeneous information networks"):
  with members:
    - "multi round influence maximization"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "contaminant removal for android malware detection systems"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-6 (induced by source paper "a self organizing tensor architecture for multi view clustering"):
  with members:
    - "joint embedding of meta path and meta graph for heterogeneous information networks"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "a survey on multi view clustering"

Connected through hyperedge HE-7 (induced by source paper "multi view unsupervised feature selection by cross diffused matrix alignment"):
  with members:
    - "unsupervised feature selection with adaptive structure learning"
    - "algorithms for learning kernels based on centered alignment"

Connected through hyperedge HE-8 (induced by source paper "a broad learning approach for context aware mobile application recommendation"):
  with members:
    - "multi view machines"

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)