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: "actor centric relation network"
  Abstract: Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. Here, we go one step further and model spatio-temporal relations to capture the interactions between human actors, relevant objects and scene elements essential to differentiate similar human actions. Our approach is weakly supervised and mines the relevant elements automatically with an actor-centric relational network (ACRN). ACRN computes and accumulates pair-wise relation information from actor and global scene features, and generates relation features for action classification. It is implemented as neural networks and can be trained jointly with an existing action detection system. We show that ACRN outperforms alternative approaches which capture relation information, and that the proposed framework improves upon the state-of-the-art performance on JHMDB and AVA. A visualization of the learned relation features confirms that our approach is able to attend to the relevant relations for each action.

Connected through hyperedge HE-1 (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"
    - "pairwise body part attention for recognizing human object interactions"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning"):
  with members:
    - "visual semantic reasoning for image text matching"
    - "use what you have video retrieval using representations from collaborative experts"
    - "language conditioned graph networks for relational reasoning"
    - "simple bert models for relation extraction and semantic role labeling"
    - "evaluating text to image matching using binary image selection bison"
    - "relation aware graph attention network for visual question answering"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "graph r cnn for scene graph generation"

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"
    - "relational inductive biases deep learning and graph networks"
    - "object level visual reasoning in videos"
    - "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 "something else compositional action recognition with spatial temporal interaction networks"):
  with members:
    - "spatio temporal action graph networks"
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "few shot video classification via temporal alignment"
    - "reasoning about human object interactions through dual attention networks"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "video action transformer network"

Connected through hyperedge HE-7 (induced by source paper "relation graph network for 3d object detection in point clouds"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "centernet keypoint triplets for object detection"
    - "fan focused attention networks"
    - "adaptively connected neural networks"
    - "learning actor relation graphs for group activity recognition"

Connected through hyperedge HE-8 (induced by source paper "stage spatio temporal attention on graph entities for video action detection"):
  with members:
    - "step spatio temporal progressive learning for video action detection"
    - "collaborative spatio temporal feature learning for video action recognition"
    - "actor conditioned attention maps for video action detection"
    - "long term feature banks for detailed video understanding"
    - "a better baseline for ava"
    - "video action transformer network"
    - "timeception for complex action recognition"
    - "a structured model for action detection"

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)