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: "adaptive convolutional elm for concept drift handling in online stream data"
  Abstract: In big data era, the data continuously generated and its distribution may keep changes overtime. These challenges in online stream of data are known as concept drift. In this paper, we proposed the Adaptive Convolutional ELM method (ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid Extreme Learning Machine (ELM) model plus adaptive capability. This method is aimed for concept drift handling. We enhanced the CNN as convolutional hiererchical features representation learner combined with Elastic ELM (E$^2$LM) as a parallel supervised classifier. We propose an Adaptive OS-ELM (AOS-ELM) for concept drift adaptability in classifier level (named ACNNELM-1) and matrices concatenation ensembles for concept drift adaptability in ensemble level (named ACNNELM-2). Our proposed Adaptive CNNELM is flexible that works well in classifier level and ensemble level while most current methods only proposed to work on either one of the levels. #R##N#We verified our method in extended MNIST data set and not MNIST data set. We set the experiment to simulate virtual drift, real drift, and hybrid drift event and we demonstrated how our CNNELM adaptability works. Our proposed method works well and gives better accuracy, computation scalability, and concept drifts adaptability compared to the regular ELM and CNN. Further researches are still required to study the optimum parameters and to use more varied image data set.

Connected through hyperedge HE-1 (induced by source paper "implicit context aware learning and discovery for streaming data analytics"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "detecting online hate speech using context aware models"
    - "object detection via aspect ratio and context aware region based convolutional networks"

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.AI(Artificial Intelligence)