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: "object level visual reasoning in videos"
  Abstract: Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context. The next open challenges in activity recognition require a level of understanding that pushes beyond this and call for models with capabilities for fine distinction and detailed comprehension of interactions between actors and objects in a scene. We propose a model capable of learning to reason about semantically meaningful spatiotemporal interactions in videos. The key to our approach is a choice of performing this reasoning at the object level through the integration of state of the art object detection networks. This allows the model to learn detailed spatial interactions that exist at a semantic, object-interaction relevant level. We evaluate our method on three standard datasets (Twenty-BN Something-Something, VLOG and EPIC Kitchens) and achieve state of the art results on all of them. Finally, we show visualizations of the interactions learned by the model, which illustrate object classes and their interactions corresponding to different activity classes.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "symbiotic attention with privileged information for egocentric action recognition"):
  with members:
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "baidu uts submission to the epic kitchens action recognition challenge 2019"
    - "an evaluation of action recognition models on epic kitchens"
    - "large scale weakly supervised pre training for video action recognition"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "actor centric relation network"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-3 (induced by source paper "classifying all interacting pairs in a single shot"):
  with members:
    - "detecting human object interactions via functional generalization"
    - "detecting unseen visual relations using analogies"
    - "ican instance centric attention network for human object interaction detection"
    - "detecting visual relationships using box attention"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "detecting and recognizing human object interactions"
    - "focal loss for dense object detection"

Connected through hyperedge HE-4 (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-5 (induced by source paper "relational action forecasting"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "moments in time dataset one million videos for event understanding"
    - "recurrent relational networks for complex relational reasoning"
    - "relational recurrent neural networks"
    - "actor centric relation network"
    - "relational inductive biases deep learning and graph networks"
    - "predicting deeper into the future of semantic segmentation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-6 (induced by source paper "large scale weakly supervised pre training for video action recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "videos as space time region graphs"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-7 (induced by source paper "recurrent space time graph neural networks"):
  with members:
    - "recurrent space time graphs for video understanding"
    - "video relationship reasoning using gated spatio temporal energy graph"
    - "universal transformers"
    - "eco efficient convolutional network for online video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "relational recurrent neural networks"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural message passing on hybrid spatio temporal visual and symbolic graphs for video understanding"):
  with members:
    - "dynamic graph modules for modeling higher order interactions in activity recognition"
    - "iterative visual reasoning beyond convolutions"
    - "structured label inference for visual understanding"
    - "actor centric relation network"
    - "video action transformer network"
    - "hybrid knowledge routed modules for large scale object detection"
    - "a structured model for action detection"
    - "learning human object interactions by graph parsing 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)