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: "learning discriminative features via label consistent neural network"
  Abstract: Deep Convolutional Neural Networks (CNN) enforces supervised information only at the output layer, and hidden layers are trained by back propagating the prediction error from the output layer without explicit supervision. We propose a supervised feature learning approach, Label Consistent Neural Network, which enforces direct supervision in late hidden layers. We associate each neuron in a hidden layer with a particular class label and encourage it to be activated for input signals from the same class. More specifically, we introduce a label consistency regularization called "discriminative representation error" loss for late hidden layers and combine it with classification error loss to build our overall objective function. This label consistency constraint alleviates the common problem of gradient vanishing and tends to faster convergence; it also makes the features derived from late hidden layers discriminative enough for classification even using a simple $k$-NN classifier, since input signals from the same class will have very similar representations. Experimental results demonstrate that our approach achieves state-of-the-art performances on several public benchmarks for action and object category recognition.

Connected through hyperedge HE-1 (induced by source paper "a study on multimodal and interactive explanations for visual question answering"):
  with members:
    - "generating natural language explanations for visual question answering using scene graphs and visual attention"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-2 (induced by source paper "unsupervised domain adaptation learning algorithm for rgb d staircase recognition"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "megadepth learning single view depth prediction from internet photos"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "estimated depth map helps image classification"
    - "simultaneous deep transfer across domains and tasks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "beyond one hot encoding lower dimensional target embedding"):
  with members:
    - "label embedding for image classification"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "hd cnn hierarchical deep convolutional neural network for large scale visual recognition"
    - "deep networks with large output spaces"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep learning face attributes in the wild"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-4 (induced by source paper "learning spatiotemporal features for infrared action recognition with 3d convolutional neural networks"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "understanding neural networks through deep visualization"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "evaluating two stream cnn for video classification"
    - "beyond short snippets deep networks for video classification"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "learning a discriminative filter bank within a cnn for fine grained recognition"):
  with members:
    - "fine grained image classification via combining vision and language"
    - "collaborative layer wise discriminative learning in deep neural networks"
    - "ssd single shot multibox detector"
    - "mask cnn localizing parts and selecting descriptors for fine grained image 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"
    - "a large scale car dataset for fine grained categorization and verification"
    - "fine grained image classification by exploring bipartite graph labels"

Connected through hyperedge HE-6 (induced by source paper "collaborative layer wise discriminative learning in deep neural networks"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "hcp a flexible cnn framework for multi label image classification"
    - "holistically nested edge detection"
    - "deep learning with s shaped rectified linear activation units"
    - "deep residual learning for image recognition"
    - "places205 vggnet models for scene recognition"
    - "training deeper convolutional networks with deep supervision"
    - "multi scale recognition with dag cnns"

Connected through hyperedge HE-7 (induced by source paper "generating visual explanations"):
  with members:
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "learning to compose neural networks for question answering"
    - "generation and comprehension of unambiguous object descriptions"
    - "compact bilinear pooling"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "deep compositional captioning describing novel object categories without paired training data"
    - "guiding long short term memory for image caption generation"

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)