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: "compressed video action recognition"
  Abstract: Training robust deep video representations has proven to be much more challenging than learning deep image representations and consequently hampered tasks like video action recognition. This is in part due to the enormous size of raw video streams, the associated amount of computation required, and the high temporal redundancy. The 'true' and interesting signal is often drowned in too much irrelevant data. Motivated by the fact that the superfluous information can be reduced by up to two orders of magnitude with video compression techniques (like H.264, HEVC, etc.), in this work, we propose to train a deep network directly on the compressed video, devoid of redundancy, rather than the traditional highly redundant RGB stream. #R##N#This representation has a higher information density and we found the training to be easier. In addition, the signals in a compressed video provide free, albeit noisy, motion information. We propose novel techniques to use them effectively. Our approach is about 4.6 times faster than a state-of-the-art 3D-CNN model, 2.7 times faster than a ResNet-152, and very easy to implement. On the task of action recognition, our approach outperforms all the other methods on the UCF-101, HMDB-51, and Charades dataset.

Connected through hyperedge HE-1 (induced by source paper "motion excited sampler video adversarial attack with sparked prior"):
  with members:
    - "sparse black box video attack with reinforcement learning"
    - "heuristic black box adversarial attacks on video recognition models"
    - "deep video inpainting"
    - "appending adversarial frames for universal video attack"
    - "sequence level semantics aggregation for video object detection"
    - "black box adversarial attacks on video recognition models"
    - "adversarial attacks for optical flow based action recognition classifiers"
    - "sparse adversarial perturbations for videos"

Connected through hyperedge HE-2 (induced by source paper "learning to structure an image with few colors"):
  with members:
    - "machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds"
    - "clustering with deep learning taxonomy and new methods"
    - "generative adversarial networks for extreme learned image compression"
    - "fast object detection in compressed video"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hybrid scene compression for visual localization"

Connected through hyperedge HE-3 (induced by source paper "rethinking motion representation residual frames with 3d convnets for better action recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "video action recognition via neural architecture searching"
    - "video classification with channel separated convolutional networks"
    - "temporal bilinear networks for video action recognition"
    - "spatio temporal channel correlation networks for action classification"
    - "slowfast networks for video recognition"
    - "tsm temporal shift module for efficient video understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "the benefits of synthetic data for action categorization"):
  with members:
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "lattice long short term memory for human action recognition"
    - "the kinetics human action video dataset"
    - "learning from synthetic data addressing domain shift for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics"):
  with members:
    - "action recognition for depth video using multi view dynamic images"
    - "temporal 3d convnets new architecture and transfer learning for video classification"
    - "learning human pose models from synthesized data for robust rgb d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "action recognition with coarse to fine deep feature integration and asynchronous fusion"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-6 (induced by source paper "listen to look action recognition by previewing audio"):
  with members:
    - "what makes training multi modal networks hard"
    - "learning to localize sound sources in visual scenes analysis and applications"
    - "multi agent reinforcement learning based frame sampling for effective untrimmed video recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "distinit learning video representations without a single labeled video"
    - "co separating sounds of visual objects"
    - "videobert a joint model for video and language representation learning"
    - "temporal cycle consistency learning"

Connected through hyperedge HE-7 (induced by source paper "scsampler sampling salient clips from video for efficient action recognition"):
  with members:
    - "video classification with channel separated convolutional networks"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "learning to separate object sounds by watching unlabeled video"
    - "adaframe adaptive frame selection for fast video recognition"
    - "slowfast networks for video recognition"
    - "the sound of pixels"
    - "cooperative learning of audio and video models from self supervised synchronization"

Connected through hyperedge HE-8 (induced by source paper "dmc net generating discriminative motion cues for fast compressed video action recognition"):
  with members:
    - "end to end learning of motion representation for video understanding"
    - "multi fiber networks for video recognition"
    - "contextual multi scale region convolutional 3d network for activity detection"
    - "stochastic adversarial video prediction"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "non local 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)