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: "activis visual exploration of industry scale deep neural network models"
  Abstract: While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.

Connected through hyperedge HE-1 (induced by source paper "beyond saliency understanding convolutional neural networks from saliency prediction on layer wise relevance propagation"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "structured attention networks"
    - "attention is all you need"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-2 (induced by source paper "a visual programming paradigm for abstract deep learning model development"):
  with members:
    - "darviz deep abstract representation visualization and verification of deep learning models"
    - "end to end learning for self driving cars"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "an empirical evaluation of deep learning on highway driving"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-3 (induced by source paper "deeptracker visualizing the training process of convolutional neural networks"):
  with members:
    - "do convolutional neural networks learn class hierarchy"
    - "cnncomparator comparative analytics of convolutional neural networks"
    - "focal loss for dense object detection"
    - "towards better analysis of deep convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "a visual technique to analyze flow of information in a machine learning system"):
  with members:
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"

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.HC(Human-Computer Interaction)