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: "places205 vggnet models for scene recognition"
  Abstract: VGGNets have turned out to be effective for object recognition in still images. However, it is unable to yield good performance by directly adapting the VGGNet models trained on the ImageNet dataset for scene recognition. This report describes our implementation of training the VGGNets on the large-scale Places205 dataset. Specifically, we train three VGGNet models, namely VGGNet-11, VGGNet-13, and VGGNet-16, by using a Multi-GPU extension of Caffe toolbox with high computational efficiency. We verify the performance of trained Places205-VGGNet models on three datasets: MIT67, SUN397, and Places205. Our trained models achieve the state-of-the-art performance o these datasets and are made public available 1 .

Connected through hyperedge HE-1 (induced by source paper "a general framework for edited video and raw video summarization"):
  with members:
    - "diversity promoting online sampling for streaming video summarization"
    - "predicting important objects for egocentric video summarization"
    - "very deep convolutional networks for large scale image recognition"
    - "learning mixtures of submodular shells with application to document summarization"

Connected through hyperedge HE-2 (induced by source paper "trisec training data unaware imperceptible security attacks on deep neural networks"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "one pixel attack for fooling deep neural networks"
    - "red attack resource efficient decision based attack for machine learning"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "fademl understanding the impact of pre processing noise filtering on adversarial machine learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "potrojan powerful neural level trojan designs in deep learning models"

Connected through hyperedge HE-3 (induced by source paper "beyond narrative description generating poetry from images by multi adversarial training"):
  with members:
    - "how images inspire poems generating classical chinese poetry from images with memory networks"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "image captioning with semantic attention"
    - "boosting image captioning with attributes"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "generating chinese classical poems with rnn encoder decoder"
    - "self critical sequence training for image captioning"

Connected through hyperedge HE-4 (induced by source paper "security for machine learning based systems attacks and challenges during training and inference"):
  with members:
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "deepsafe a data driven approach for checking adversarial robustness in neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "cryptodl deep neural networks over encrypted data"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "safetynets verifiable execution of deep neural networks on an untrusted cloud"

Connected through hyperedge HE-5 (induced by source paper "second order democratic aggregation"):
  with members:
    - "a deeper look at power normalizations"
    - "statistically motivated second order pooling"
    - "is second order information helpful for large scale visual recognition"
    - "improved bilinear pooling with cnns"
    - "second order convolutional neural networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "interferences in match kernels"
    - "compact bilinear pooling"

Connected through hyperedge HE-6 (induced by source paper "a deeper look at power normalizations"):
  with members:
    - "improved bilinear pooling with cnns"
    - "domain adaptation by mixture of alignments of second or higher order scatter tensors"
    - "a riemannian network for spd matrix learning"
    - "bilinear cnn models for fine grained visual recognition"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "integrating deep features for material recognition"

Connected through hyperedge HE-7 (induced by source paper "forecasting user attention during everyday mobile interactions using device integrated and wearable sensors"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "mpiigaze real world dataset and deep appearance based gaze estimation"
    - "going deeper into first person activity recognition"
    - "conditional random fields as recurrent neural networks"
    - "appearance based gaze estimation in the wild"
    - "the evolution of first person vision methods a survey"
    - "tabletgaze unconstrained appearance based gaze estimation in mobile tablets"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "knowledge guided disambiguation for large scale scene classification with multi resolution cnns"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "locally supervised deep hybrid model for scene recognition"
    - "transferring object scene convolutional neural networks for event recognition in still images"
    - "real time action recognition with enhanced motion vector cnns"
    - "learning contextual dependence with convolutional hierarchical recurrent neural networks"
    - "places an image database for deep scene understanding"
    - "weakly supervised fine grained categorization with part based image representation"

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)