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 short note about kinetics 600"
  Abstract: We describe an extension of the DeepMind Kinetics human action dataset from 400 classes, each with at least 400 video clips, to 600 classes, each with at least 600 video clips. In order to scale up the dataset we changed the data collection process so it uses multiple queries per class, with some of them in a language other than english -- portuguese. This paper details the changes between the two versions of the dataset and includes a comprehensive set of statistics of the new version as well as baseline results using the I3D neural network architecture. The paper is a companion to the release of the ground truth labels for the public test set.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles"
    - "a survey of deep learning based object detection"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"

Connected through hyperedge HE-3 (induced by source paper "temporal interlacing network"):
  with members:
    - "aggregation via separation boosting facial landmark detector with semi supervised style translation"
    - "multi moments in time learning and interpreting models for multi action video understanding"
    - "deformable convnets v2 more deformable better results"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "ctm collaborative temporal modeling for action recognition"):
  with members:
    - "stm spatiotemporal and motion encoding for action recognition"
    - "video classification with channel separated convolutional networks"
    - "learning spatio temporal representation with local and global diffusion"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "eco efficient convolutional network for online video understanding"
    - "multi fiber networks for video recognition"
    - "stnet local and global spatial temporal modeling for action recognition"
    - "tsm temporal shift module for efficient video understanding"

Connected through hyperedge HE-5 (induced by source paper "top 1 solution of multi moments in time challenge 2019"):
  with members:
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "improving pairwise ranking for multi label image classification"
    - "sgdr stochastic gradient descent with restarts"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "adversarial video generation on complex datasets"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "dance dance generation motion transfer for internet videos"
    - "generating diverse high fidelity images with vq vae 2"
    - "videoflow a flow based generative model for video"
    - "generating long sequences with sparse transformers"
    - "scaling autoregressive video models"
    - "cgans with projection discriminator"
    - "video to video synthesis"

Connected through hyperedge HE-7 (induced by source paper "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"):
  with members:
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "deep high resolution representation learning for human pose estimation"
    - "elastic improving cnns with dynamic scaling policies"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi fiber networks for video recognition"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-8 (induced by source paper "temporal attentive alignment for large scale video domain adaptation"):
  with members:
    - "learning transferable self attentive representations for action recognition in untrimmed videos with weak supervision"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "learning correspondence from the cycle consistency of time"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "open set domain adaptation by backpropagation"
    - "mind the class weight bias weighted maximum mean discrepancy for unsupervised domain adaptation"
    - "learning spatio temporal representation with pseudo 3d residual 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)