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: "exploiting image trained cnn architectures for unconstrained video classification"
  Abstract: We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling, feature normalization, choice of CNN layers as well as choice of classifiers. Making judicious choices along these dimensions led to a very significant increase in performance over more naive approaches that have been used till now. We evaluate our approach on the challenging TRECVID MED'14 dataset with two popular CNN architectures pretrained on ImageNet. On this MED'14 dataset, our methods, based entirely on image-trained CNN features, can outperform several state-of-the-art non-CNN models. Our proposed late fusion of CNN- and motion-based features can further increase the mean average precision (mAP) on MED'14 from 34.95% to 38.74%. The fusion approach achieves the state-of-the-art classification performance on the challenging UCF-101 dataset.

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "memory augmented temporal dynamic learning for action recognition"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "motion appearance co memory networks for video question answering"
    - "learning dynamic memory networks for object tracking"
    - "memory warps for learning long term online video representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lattice long short term memory for human action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "ts lstm and temporal inception exploiting spatiotemporal dynamics for activity recognition"

Connected through hyperedge HE-4 (induced by source paper "p odn prototype based open deep network for open set recognition"):
  with members:
    - "robust classification with convolutional prototype learning"
    - "generative openmax for multi class open set classification"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"
    - "learning long term dependencies for action recognition with a biologically inspired deep network"
    - "temporal segment networks towards good practices for deep action recognition"
    - "tensorflow a system for large scale machine learning"
    - "towards open set deep networks"
    - "towards open world recognition"

Connected through hyperedge HE-5 (induced by source paper "odn opening the deep network for open set action recognition"):
  with members:
    - "temporal segment networks towards good practices for deep action recognition"
    - "towards open set deep networks"
    - "towards open world recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "two stream convolutional networks for action recognition in videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-6 (induced by source paper "recurrent residual module for fast inference in videos"):
  with members:
    - "moments in time dataset one million videos for event understanding"
    - "performance guaranteed network acceleration via high order residual quantization"
    - "what actions are needed for understanding human actions in videos"
    - "predictive corrective networks for action detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "the kinetics human action video dataset"
    - "the power of sparsity in convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "yotube searching action proposal via recurrent and static regression networks"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "r c3d region convolutional 3d network for temporal activity detection"
    - "deep learning for video classification and captioning"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "searching action proposals via spatial actionness estimation and temporal path inference and tracking"
    - "object detection from video tubelets with convolutional neural networks"
    - "ssd single shot multibox detector"
    - "deep learning for detecting multiple space time action tubes in videos"

Connected through hyperedge HE-8 (induced by source paper "vision based navigation of autonomous vehicle in roadway environments with unexpected hazards"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "on the exploration of convolutional fusion networks for visual recognition"
    - "lost and found detecting small road hazards for self driving vehicles"
    - "end to end learning for self driving cars"
    - "towards evaluating the robustness of neural networks"

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)