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: "deep fishernet for object classification"
  Abstract: Despite the great success of convolutional neural networks (CNN) for the image classification task on datasets like Cifar and ImageNet, CNN's representation power is still somewhat limited in dealing with object images that have large variation in size and clutter, where Fisher Vector (FV) has shown to be an effective encoding strategy. FV encodes an image by aggregating local descriptors with a universal generative Gaussian Mixture Model (GMM). FV however has limited learning capability and its parameters are mostly fixed after constructing the codebook. To combine together the best of the two worlds, we propose in this paper a neural network structure with FV layer being part of an end-to-end trainable system that is differentiable; we name our network FisherNet that is learnable using backpropagation. Our proposed FisherNet combines convolutional neural network training and Fisher Vector encoding in a single end-to-end structure. We observe a clear advantage of FisherNet over plain CNN and standard FV in terms of both classification accuracy and computational efficiency on the challenging PASCAL VOC object classification task.

Connected through hyperedge HE-1 (induced by source paper "dynamonet dynamic action and motion network"):
  with members:
    - "visual dynamics stochastic future generation via layered cross convolutional networks"
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "self supervised learning of face representations for video face clustering"
    - "learning human optical flow"
    - "spatio temporal channel correlation networks for action classification"
    - "cooperative learning of audio and video models from self supervised synchronization"
    - "d3d distilled 3d networks for video action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "interpretable bow networks for adversarial example detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learnable histogram statistical context features for deep neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "deep attentional structured representation learning for visual recognition"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization"):
  with members:
    - "cbam convolutional block attention module"
    - "is second order information helpful for large scale visual recognition"
    - "squeeze and excitation networks"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "towards effective codebookless model for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep ten texture encoding network"

Connected through hyperedge HE-4 (induced by source paper "large scale holistic video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "dynamonet dynamic action and motion network"
    - "distinit learning video representations without a single labeled video"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"

Connected through hyperedge HE-5 (induced by source paper "holistic large scale video understanding"):
  with members:
    - "self supervised learning of face representations for video face clustering"
    - "dynamonet dynamic action and motion network"
    - "slowfast networks for video recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-6 (induced by source paper "efficient codebook and factorization for second order representation learning"):
  with members:
    - "cross modal retrieval in the cooking context learning semantic text image embeddings"
    - "leveraging implicit spatial information in global features for image retrieval"
    - "deep metric learning with hierarchical triplet loss"
    - "sampling matters in deep embedding learning"
    - "no fuss distance metric learning using proxies"
    - "hadamard product for low rank bilinear pooling"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep embeddings with histogram loss"

Connected through hyperedge HE-7 (induced by source paper "approximating cnns with bag of local features models works surprisingly well on imagenet"):
  with members:
    - "this looks like that deep learning for interpretable image recognition"
    - "scale coding bag of deep features for human attribute and action recognition"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "deep patch learning for weakly supervised object classification and discovery"
    - "hcp a flexible cnn framework for multi label image classification"
    - "bags of local convolutional features for scalable instance search"

Connected through hyperedge HE-8 (induced by source paper "local area transform for cross modality correspondence matching and deep scene recognition"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "fcss fully convolutional self similarity for dense semantic correspondence"
    - "scnet learning semantic correspondence"
    - "semantic clustering for robust fine grained scene recognition"
    - "convolutional color constancy"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "deep residual learning for image recognition"
    - "color constancy using cnns"

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)