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: "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
  Abstract: Classifying videos according to content semantics is an important problem with a wide range of applications. In this paper, we propose a hybrid deep learning framework for video classification, which is able to model static spatial information, short-term motion, as well as long-term temporal clues in the videos. Specifically, the spatial and the short-term motion features are extracted separately by two Convolutional Neural Networks (CNN). These two types of CNN-based features are then combined in a regularized feature fusion network for classification, which is able to learn and utilize feature relationships for improved performance. In addition, Long Short Term Memory (LSTM) networks are applied on top of the two features to further model longer-term temporal clues. The main contribution of this work is the hybrid learning framework that can model several important aspects of the video data. We also show that (1) combining the spatial and the short-term motion features in the regularized fusion network is better than direct classification and fusion using the CNN with a softmax layer, and (2) the sequence-based LSTM is highly complementary to the traditional classification strategy without considering the temporal frame orders. Extensive experiments are conducted on two popular and challenging benchmarks, the UCF-101 Human Actions and the Columbia Consumer Videos (CCV). On both benchmarks, our framework achieves to-date the best reported performance: $91.3\%$ on the UCF-101 and $83.5\%$ on the CCV.

Connected through hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination"):
  with members:
    - "self supervised spatio temporal representation learning for videos by predicting motion and appearance statistics"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "contrastive multiview coding"
    - "universal dependency parsing from scratch"
    - "dynamonet dynamic action and motion network"
    - "learning correspondence from the cycle consistency of time"
    - "videobert a joint model for video and language representation learning"
    - "howto100m learning a text video embedding by watching hundred million narrated video clips"

Connected through hyperedge HE-2 (induced by source paper "ttpp temporal transformer with progressive prediction for efficient action anticipation"):
  with members:
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "attention aware compositional network for person re identification"
    - "video action transformer network"
    - "temporal recurrent networks for online action detection"
    - "attention driven person re identification"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "encouraging lstms to anticipate actions very early"
    - "red reinforced encoder decoder networks for action anticipation"

Connected through hyperedge HE-3 (induced by source paper "a hybrid rnn hmm approach for weakly supervised temporal action segmentation"):
  with members:
    - "weakly supervised action segmentation with iterative soft boundary assignment"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "the kinetics human action video dataset"
    - "connectionist temporal modeling for weakly supervised action labeling"

Connected through hyperedge HE-4 (induced by source paper "two stream video classification with cross modality attention"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual attention network for scene segmentation"
    - "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"
    - "non local neural networks"

Connected through hyperedge HE-5 (induced by source paper "two stream action recognition oriented video super resolution"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "the perception distortion tradeoff"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-6 (induced by source paper "identifying and resisting adversarial videos using temporal consistency"):
  with members:
    - "adversarial framing for image and video classification"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "sparse adversarial perturbations for videos"
    - "max mahalanobis linear discriminant analysis networks"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-7 (induced by source paper "scr graph spatial causal relationships based graph reasoning network for human action prediction"):
  with members:
    - "peeking into the future predicting future person activities and locations in videos"
    - "graph neural networks a review of methods and applications"
    - "situation recognition with graph neural networks"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "mask r cnn"
    - "the more you know using knowledge graphs for image classification"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "simultaneous segmentation and recognition towards more accurate ego gesture recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "analysis of hand segmentation in the wild"
    - "egocentric gesture recognition for head mounted ar devices"
    - "learning to estimate 3d hand pose from single rgb images"
    - "the something something video database for learning and evaluating visual common sense"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "deep residual learning for image recognition"
    - "exact solutions to the nonlinear dynamics of learning in deep linear 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)