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: "spatio temporal action graph networks"
  Abstract: Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance. Activity recognition models that represent object interactions explicitly have the potential to learn in a more efficient manner than those that represent scenes with global descriptors. We propose a novel inter-object graph representation for activity recognition based on a disentangled graph embedding with direct observation of edge appearance. We employ a novel factored embedding of the graph structure, disentangling a representation hierarchy formed over spatial dimensions from that found over temporal variation. We demonstrate the effectiveness of our model on the Charades activity recognition benchmark, as well as a new dataset of driving activities focusing on multi-object interactions with near-collision events. Our model offers significantly improved performance compared to baseline approaches without object-graph representations, or with previous graph-based models.

Connected through hyperedge HE-1 (induced by source paper "learning canonical representations for scene graph to image generation"):
  with members:
    - "interactive image generation using scene graphs"
    - "image synthesis from reconfigurable layout and style"
    - "on the universality of invariant networks"
    - "mirrorgan learning text to image generation by redescription"
    - "text guided person image synthesis"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "specifying object attributes and relations in interactive scene generation"

Connected through hyperedge HE-2 (induced by source paper "something else compositional action recognition with spatial temporal interaction networks"):
  with members:
    - "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"
    - "actor centric relation network"
    - "video action transformer network"

Connected through hyperedge HE-3 (induced by source paper "differentiable scene graphs"):
  with members:
    - "dynamic graph attention for referring expression comprehension"
    - "generating easy to understand referring expressions for target identifications"
    - "vrr vg refocusing visually relevant relationships"
    - "learning canonical representations for scene graph to image generation"
    - "adaptive reconstruction network for weakly supervised referring expression grounding"
    - "specifying object attributes and relations in interactive scene generation"
    - "scene graph prediction with limited labels"
    - "triplet aware scene graph embeddings"

Connected through hyperedge HE-4 (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"
    - "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"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"
    - "graph r cnn for scene graph generation"

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)