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: "interactive inter layer activeness propagation"
  Abstract: An increasing number of computer vision tasks can be tackled with deep features, which are the intermediate outputs of a pre-trained Convolutional Neural Network. Despite the astonishing performance, deep features extracted from low-level neurons are still below satisfaction, arguably because they cannot access the spatial context contained in the higher layers. In this paper, we present InterActive, a novel algorithm which computes the activeness of neurons and network connections. Activeness is propagated through a neural network in a top-down manner, carrying high-level context and improving the descriptive power of low-level and mid-level neurons. Visualization indicates that neuron activeness can be interpreted as spatial-weighted neuron responses. We achieve state-of-the-art classification performance on a wide range of image datasets.

Connected through hyperedge HE-1 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"

Connected through hyperedge HE-2 (induced by source paper "transform invariant convolutional neural networks for image classification and search"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "learning attributes equals multi source domain generalization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "contextual action recognition with r cnn"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "deepfont identify your font from an image"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "multi scale spatially asymmetric recalibration for image classification"):
  with members:
    - "snapshot ensembles train 1 get m for free"
    - "sort second order response transform for visual recognition"
    - "mixup beyond empirical risk minimization"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "shake shake regularization"
    - "stacked hourglass networks for human pose estimation"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "object part attention model for fine grained image classification"):
  with members:
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "fine grained image classification by exploring bipartite graph labels"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-5 (induced by source paper "adversarial soft detection based aggregation network for image retrieval"):
  with members:
    - "unsupervised semantic based aggregation of deep convolutional features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting oxford and paris large scale image retrieval benchmarking"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "focal loss for dense object detection"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "fine tuning cnn image retrieval with no human annotation"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-6 (induced by source paper "instance search via instance level segmentation and feature representation"):
  with members:
    - "deformable convolutional networks"
    - "faster r cnn features for instance search"
    - "aggregated residual transformations for deep neural networks"
    - "good practice in cnn feature transfer"
    - "fully convolutional instance aware semantic segmentation"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"):
  with members:
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "recent advance in content based image retrieval a literature survey"
    - "mask r cnn"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "fully convolutional instance aware semantic segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "netvlad cnn architecture for weakly supervised place recognition"

Connected through hyperedge HE-8 (induced by source paper "feedback prop convolutional neural network inference under partial evidence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "network dissection quantifying interpretability of deep visual representations"
    - "attribute recognition by joint recurrent learning of context and correlation"
    - "opening the black box of deep neural networks via information"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "automatic attribute discovery with neural activations"
    - "layer wise relevance propagation for neural networks with local renormalization layers"
    - "top down neural attention by excitation backprop"

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.CV(Computer Vision and Pattern Recognition)