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: "classifying collisions with spatio temporal action graph networks"
  Abstract: Events defined by the interaction of objects in a scene often are of critical importance, yet such events are typically rare and available labeled examples insufficient to train a conventional deep model that performs well across expected object appearances. Most deep learning activity recognition models focus on global context aggregation and do not explicitly consider object interactions inside the video, potentially overlooking important cues relevant to interpreting activity in the scene. In this paper, we show that a new model for explicit representation of object interactions significantly improves deep video activity classification for driving collision detection. We propose a Spatio-Temporal Action Graph (STAG) network, which incorporates spatial and temporal relations of objects. The network is automatically learned from data, with a latent graph structure inferred for the task. As a benchmark to evaluate performance on collision detection tasks, we introduce a novel data set based on data obtained from real life driving collisions and near-collisions. This data set reflects the challenging task of detecting and classifying accidents in a richly varying but yet highly constrained setting, that is very relevant to the evaluation of autonomous driving and alerting systems. Our experiments confirm that our STAG model offers significantly improved results for collision activity classification.

Connected through hyperedge HE-1 (induced by source paper "language conditioned graph networks for relational reasoning"):
  with members:
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "murel multimodal relational reasoning for visual question answering"
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "neighbourhood watch referring expression comprehension via language guided graph attention networks"
    - "mattnet modular attention network for referring expression comprehension"

Connected through hyperedge HE-2 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised traffic accident detection in first person videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "joint person segmentation and identification in synchronized first and third person videos"

Connected through hyperedge HE-4 (induced by source paper "video action transformer network"):
  with members:
    - "image transformer"
    - "videocapsulenet a simplified network for action detection"
    - "yh technologies at activitynet challenge 2018"
    - "actor centric relation network"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)