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 visual analytics of text streams"
  Abstract: We present an online visual analytics approach to helping users explore and understand hierarchical topic evolution in high-volume text streams. The key idea behind this approach is to identify representative topics in incoming documents and align them with the existing representative topics that they immediately follow (in time). To this end, we learn a set of streaming tree cuts from topic trees based on user-selected focus nodes. A dynamic Bayesian network model has been developed to derive the tree cuts in the incoming topic trees to balance the fitness of each tree cut and the smoothness between adjacent tree cuts. By connecting the corresponding topics at different times, we are able to provide an overview of the evolving hierarchical topics. A sedimentation-based visualization has been designed to enable the interactive analysis of streaming text data from global patterns to local details. We evaluated our method on real-world datasets and the results are generally favorable.

Connected through hyperedge HE-1 (induced by source paper "an incremental dimensionality reduction method for visualizing streaming multidimensional data"):
  with members:
    - "progressive data science potential and challenges"
    - "an overview of gradient descent optimization algorithms"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "adadelta an adaptive learning rate method"
    - "gmap drawing graphs as maps"

Connected through hyperedge HE-2 (induced by source paper "big data meet cyber physical systems a panoramic survey"):
  with members:
    - "information and communications technologies for sustainable development goals state of the art needs and perspectives"
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "developing an edge computing platform for real time descriptive analytics"
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "wireless device to device caching networks basic principles and system performance"
    - "social computing for mobile big data"
    - "streaming big data meets backpressure in distributed network computation"
    - "big data caching for networking moving from cloud to edge"

Connected through hyperedge HE-3 (induced by source paper "recent research advances on interactive machine learning"):
  with members:
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "the subspace voyager exploring high dimensional data along a continuum of salient 3d subspaces"
    - "do convolutional neural networks learn class hierarchy"
    - "analyzing the noise robustness of deep neural networks"
    - "visual analytics for explainable deep learning"
    - "understanding hidden memories of recurrent neural networks"
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "towards better analysis of deep convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "computer assisted text analysis for social science topic models and beyond"):
  with members:
    - "efficient estimation of word representations in vector space"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "the author topic model for authors and documents"
    - "topic models conditioned on arbitrary features with dirichlet multinomial regression"

Connected through hyperedge HE-5 (induced by source paper "towards better analysis of machine learning models a visual analytics perspective"):
  with members:
    - "towards better analysis of deep convolutional neural networks"
    - "graying the black box understanding dqns"
    - "approximated and user steerable tsne for progressive visual analytics"
    - "an uncertainty aware approach for exploratory microblog retrieval"
    - "active learning with statistical models"

Connected through hyperedge HE-6 (induced by source paper "towards better analysis of deep convolutional neural networks"):
  with members:
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the depth of deep neural networks a theoretical view"
    - "understanding intra class knowledge inside cnn"

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.IR(Information Retrieval)