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: "ava a video dataset of spatio temporally localized atomic visual actions"
  Abstract: This paper introduces a video dataset of spatio-temporally localized Atomic Visual Actions (AVA). The AVA dataset densely annotates 80 atomic visual actions in 430 15-minute video clips, where actions are localized in space and time, resulting in 1.58M action labels with multiple labels per person occurring frequently. The key characteristics of our dataset are: (1) the definition of atomic visual actions, rather than composite actions; (2) precise spatio-temporal annotations with possibly multiple annotations for each person; (3) exhaustive annotation of these atomic actions over 15-minute video clips; (4) people temporally linked across consecutive segments; and (5) using movies to gather a varied set of action representations. This departs from existing datasets for spatio-temporal action recognition, which typically provide sparse annotations for composite actions in short video clips. We will release the dataset publicly. #R##N#AVA, with its realistic scene and action complexity, exposes the intrinsic difficulty of action recognition. To benchmark this, we present a novel approach for action localization that builds upon the current state-of-the-art methods, and demonstrates better performance on JHMDB and UCF101-24 categories. While setting a new state of the art on existing datasets, the overall results on AVA are low at 15.6% mAP, underscoring the need for developing new approaches for video understanding.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal ranked attention networks for video captioning"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "object aware aggregation with bidirectional temporal graph for video captioning"
    - "less is more picking informative frames for video captioning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "the sound of pixels"
    - "improved image captioning via policy gradient optimization of spider"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-2 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition"):
  with members:
    - "hallucinating optical flow features for video classification"
    - "few shot learning via saliency guided hallucination of samples"
    - "assemblenet searching for multi stream neural connectivity in video architectures"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "deeply supervised salient object detection with short connections"
    - "learning discriminative video representations using adversarial perturbations"
    - "evolving space time neural architectures for videos"
    - "a deeper look at power normalizations"

Connected through hyperedge HE-3 (induced by source paper "evolving losses for unsupervised video representation learning"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "moments in time dataset one million videos for event understanding"
    - "deep clustering for unsupervised learning of visual features"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised learning via meta learning"
    - "autoaugment learning augmentation policies from data"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (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-5 (induced by source paper "weakly supervised temporal action localization using deep metric learning"):
  with members:
    - "w talc weakly supervised temporal activity localization and classification"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "temporal action detection with structured segment networks"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-6 (induced by source paper "actions as moving points"):
  with members:
    - "tacnet transition aware context network for spatio temporal action detection"
    - "centernet keypoint triplets for object detection"
    - "step spatio temporal progressive learning for video action detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-7 (induced by source paper "action segmentation with joint self supervised temporal domain adaptation"):
  with members:
    - "temporal attentive alignment for video domain adaptation"
    - "temporal attentive alignment for large scale video domain adaptation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "ms tcn multi stage temporal convolutional network for action segmentation"
    - "attending to discriminative certainty for domain adaptation"
    - "human action recognition and prediction a survey"
    - "learning motion in feature space locally consistent deformable convolution networks for fine grained action detection"
    - "multi adversarial domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "object detection as a positive unlabeled problem"):
  with members:
    - "an empirical study of example forgetting during deep neural network learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"
    - "soft sampling for robust object detection"
    - "positive unlabeled learning with non negative risk estimator"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"

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)