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: "attention transfer from web images for video recognition"
  Abstract: Training deep learning based video classifiers for action recognition requires a large amount of labeled videos. The labeling process is labor-intensive and time-consuming. On the other hand, large amount of weakly-labeled images are uploaded to the Internet by users everyday. To harness the rich and highly diverse set of Web images, a scalable approach is to crawl these images to train deep learning based classifier, such as Convolutional Neural Networks (CNN). However, due to the domain shift problem, the performance of Web images trained deep classifiers tend to degrade when directly deployed to videos. One way to address this problem is to fine-tune the trained models on videos, but sufficient amount of annotated videos are still required. In this work, we propose a novel approach to transfer knowledge from image domain to video domain. The proposed method can adapt to the target domain (i.e. video data) with limited amount of training data. Our method maps the video frames into a low-dimensional feature space using the class-discriminative spatial attention map for CNNs. We design a novel Siamese EnergyNet structure to learn energy functions on the attention maps by jointly optimizing two loss functions, such that the attention map corresponding to a ground truth concept would have higher energy. We conduct extensive experiments on two challenging video recognition datasets (i.e. TVHI and UCF101), and demonstrate the efficacy of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "video storytelling textual summaries for events"):
  with members:
    - "ffnet video fast forwarding via reinforcement learning"
    - "predicting visual features from text for image and video caption retrieval"
    - "unsupervised learning of view invariant action representations"
    - "query adaptive video summarization via quality aware relevance estimation"
    - "improved image captioning via policy gradient optimization of spider"
    - "dual glance model for deciphering social relationships"
    - "summarization of user generated sports video by using deep action recognition features"
    - "recurrent topic transition gan for visual paragraph generation"

Connected through hyperedge HE-2 (induced by source paper "deep image to video adaptation and fusion networks for action recognition"):
  with members:
    - "hierarchically learned view invariant representations for cross view action recognition"
    - "combined static and motion features for deep networks based activity recognition in videos"
    - "transferable feature representation for visible to infrared cross dataset human action recognition"
    - "global temporal representation based cnns for infrared action recognition"
    - "deep cross media knowledge transfer"
    - "exploiting images for video recognition with hierarchical generative adversarial networks"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "semantic autoencoder for zero shot learning"

Connected through hyperedge HE-3 (induced by source paper "image to video domain adaptation using web supervision"):
  with members:
    - "importance weighted adversarial nets for partial domain adaptation"
    - "slowfast networks for video recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-4 (induced by source paper "webly supervised joint embedding for cross modal image text retrieval"):
  with members:
    - "exploring stereotypes and biased data with the crowd"
    - "sampling matters in deep embedding learning"
    - "learning two branch neural networks for image text matching tasks"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "dual path convolutional image text embedding with instance loss"
    - "learning robust visual semantic embeddings"
    - "vse improving visual semantic embeddings with hard negatives"
    - "learning social image embedding with deep multimodal attention networks"

Connected through hyperedge HE-5 (induced by source paper "gradmix multi source transfer across domains and tasks"):
  with members:
    - "direction concentration learning enhancing congruency in machine learning"
    - "semi supervised domain adaptation via minimax entropy"
    - "mixmatch a holistic approach to semi supervised learning"
    - "taskonomy disentangling task transfer learning"
    - "deep cocktail network multi source unsupervised domain adaptation with category shift"
    - "boosting domain adaptation by discovering latent domains"
    - "meta transfer learning for few shot learning"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-6 (induced by source paper "rdpd rich data helps poor data via imitation"):
  with members:
    - "data distillation towards omni supervised learning"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "a structured self attentive sentence embedding"
    - "distilling a neural network into a soft decision tree"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "deep model compression distilling knowledge from noisy teachers"
    - "convolutional recurrent neural networks for music classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "hacs human action clips and segments dataset for recognition and temporal localization"):
  with members:
    - "a short note about kinetics 600"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "ctap complementary temporal action proposal generation"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)