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: "what happened to my dog in that network unraveling top down generators in convolutional neural networks"
  Abstract: Top-down information plays a central role in human perception, but plays relatively little role in many current state-of-the-art deep networks, such as Convolutional Neural Networks (CNNs). This work seeks to explore a path by which top-down information can have a direct impact within current deep networks. We explore this path by learning and using "generators" corresponding to the network internal effects of three types of transformation (each a restriction of a general affine transformation): rotation, scaling, and translation. We demonstrate how these learned generators can be used to transfer top-down information to novel settings, as mediated by the "feature flows" that the transformations (and the associated generators) correspond to inside the network. Specifically, we explore three aspects: 1) using generators as part of a method for synthesizing transformed images --- given a previously unseen image, produce versions of that image corresponding to one or more specified transformations, 2) "zero-shot learning" --- when provided with a feature flow corresponding to the effect of a transformation of unknown amount, leverage learned generators as part of a method by which to perform an accurate categorization of the amount of transformation, even for amounts never observed during training, and 3) (inside-CNN) "data augmentation" --- improve the classification performance of an existing network by using the learned generators to directly provide additional training "inside the CNN".

Connected through hyperedge HE-1 (induced by source paper "mining interpretable aog representations from convolutional networks via active question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "examining cnn representations with respect to dataset bias"
    - "mining object parts from cnns via active question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "harnessing deep neural networks with logic rules"
    - "growing interpretable part graphs on convnets via multi shot learning"

Connected through hyperedge HE-2 (induced by source paper "controllable top down feature transformer"):
  with members:
    - "universal style transfer via feature transforms"
    - "growing interpretable part graphs on convnets via multi shot learning"
    - "single image 3d interpreter network"
    - "inverse compositional spatial transformer networks"
    - "holistically nested edge detection"
    - "a neural algorithm of artistic style"
    - "spatial transformer networks"
    - "deep convolutional inverse graphics network"

Connected through hyperedge HE-3 (induced by source paper "growing interpretable part graphs on convnets via multi shot learning"):
  with members:
    - "inverting visual representations with convolutional networks"
    - "unsupervised learning on neural network outputs"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "deep learning for semantic part segmentation with high level guidance"
    - "understanding deep features with computer generated imagery"
    - "fast r cnn"
    - "unsupervised learning on neural network outputs with application in zero shot learning"

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.NE(Neural and Evolutionary Computing)