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: "a better baseline for ava"
  Abstract: We introduce a simple baseline for action localization on the AVA dataset. The model builds upon the Faster R-CNN bounding box detection framework, adapted to operate on pure spatiotemporal features - in our case produced exclusively by an I3D model pretrained on Kinetics. This model obtains 21.9% average AP on the validation set of AVA v2.1, up from 14.5% for the best RGB spatiotemporal model used in the original AVA paper (which was pretrained on Kinetics and ImageNet), and up from 11.3 of the publicly available baseline using a ResNet101 image feature extractor, that was pretrained on ImageNet. Our final model obtains 22.8%/21.9% mAP on the val/test sets and outperforms all submissions to the AVA challenge at CVPR 2018.

Connected through hyperedge HE-1 (induced by source paper "action genome actions as composition of spatio temporal scene graphs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a short note on the kinetics 700 human action dataset"
    - "graphical contrastive losses for scene graph parsing"
    - "protogan towards few shot learning for action recognition"
    - "tarn temporal attentive relation network for few shot and zero shot action recognition"
    - "visual relationships as functions enabling few shot scene graph prediction"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"

Connected through hyperedge HE-2 (induced by source paper "distinit learning video representations without a single labeled video"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "taskonomy disentangling task transfer learning"
    - "video action transformer network"
    - "unsupervised representation learning by predicting image rotations"
    - "spatio temporal channel correlation networks for action classification"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "stage spatio temporal attention on graph entities for video action detection"):
  with members:
    - "step spatio temporal progressive learning for video action detection"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "actor conditioned attention maps for video action detection"
    - "long term feature banks for detailed video understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "timeception for complex action recognition"
    - "a structured model for action detection"

Connected through hyperedge HE-4 (induced by source paper "dance with flow two in one stream action detection"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "videocapsulenet a simplified network for action detection"
    - "actor centric relation network"
    - "modulating early visual processing by language"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-5 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-6 (induced by source paper "epic fusion audio visual temporal binding for egocentric action recognition"):
  with members:
    - "learning to localize sound source in visual scenes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning a text video embedding from incomplete and heterogeneous data"
    - "seeing voices and hearing faces cross modal biometric matching"
    - "learnable pins cross modal embeddings for person identity"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "2 5d visual sound"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-7 (induced by source paper "golfdb a video database for golf swing sequencing"):
  with members:
    - "rethinking imagenet pre training"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "soccernet a scalable dataset for action spotting in soccer videos"
    - "real time end to end action detection with two stream networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "what actions are needed for understanding human actions in videos"
    - "temporal action detection with structured segment networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "three branches detecting actions with richer features"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "a short note about kinetics 600"
    - "yh technologies at activitynet challenge 2018"
    - "video action transformer network"
    - "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"
    - "focal loss for dense object detection"

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)