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: "hd cnn hierarchical deep convolutional neural network for large scale visual recognition"
  Abstract: In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However, existing deep convolutional neural networks (CNN) are trained as flat N-way classifiers, and few efforts have been made to leverage the hierarchical structure of categories. In this paper, we introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers. During HD-CNN training, component-wise pretraining is followed by global finetuning with a multinomial logistic loss regularized by a coarse category consistency term. In addition, conditional executions of fine category classifiers and layer parameter compression make HD-CNNs scalable for large-scale visual recognition. We achieve state-of-the-art results on both CIFAR100 and large-scale ImageNet 1000-class benchmark datasets. In our experiments, we build up three different HD-CNNs and they lower the top-1 error of the standard CNNs by 2.65%, 3.1% and 1.1%, respectively.

Connected through hyperedge HE-1 (induced by source paper "integrating domain knowledge using hierarchies to improve deep classifiers"):
  with members:
    - "hierarchy based image embeddings for semantic image retrieval"
    - "not just a matter of semantics the relationship between visual similarity and semantic similarity"
    - "do convolutional neural networks learn class hierarchy"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "progressive neural architecture search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "the inaturalist species classification and detection dataset"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "incremental learning using a grow and prune paradigm with efficient neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "knowledge transfer in deep block modular neural networks"
    - "scann synthesis of compact and accurate neural networks"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "neural voice cloning with a few samples"
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-3 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-4 (induced by source paper "hierarchy based image embeddings for semantic image retrieval"):
  with members:
    - "label embedding network learning label representation for soft training of deep networks"
    - "random erasing data augmentation"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "learning structured semantic embeddings for visual recognition"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "sgdr stochastic gradient descent with restarts"
    - "yolo9000 better faster stronger"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "hierarchical auxiliary learning"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "hierarchical loss for classification"
    - "b cnn branch convolutional neural network for hierarchical classification"
    - "shake shake regularization"
    - "sgdr stochastic gradient descent with restarts"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

Connected through hyperedge HE-6 (induced by source paper "huse hierarchical universal semantic embeddings"):
  with members:
    - "adversarial representation learning for text to image matching"
    - "polysemous visual semantic embedding for cross modal retrieval"
    - "hierarchy based image embeddings for semantic image retrieval"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "vse improving visual semantic embeddings with hard negatives"
    - "an overview of cross media retrieval concepts methodologies benchmarks and challenges"

Connected through hyperedge HE-7 (induced by source paper "do convolutional neural networks learn class hierarchy"):
  with members:
    - "towards better analysis of machine learning models a visual analytics perspective"
    - "direct manipulation visualization of deep networks"
    - "towards better analysis of deep convolutional neural networks"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "decision forests convolutional networks and the models in between"
    - "visual analysis of hidden state dynamics in recurrent neural networks"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "contemplating visual emotions understanding and overcoming dataset bias"):
  with members:
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "webvision database visual learning and understanding from web data"
    - "collaborative summarization of topic related videos"
    - "automatic understanding of image and video advertisements"
    - "multi task curriculum transfer deep learning of clothing attributes"

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)