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: "single image action recognition using semantic body part actions"
  Abstract: In this paper, we propose a novel single image action recognition algorithm which is based on the idea of semantic body part actions. Unlike existing bottom up methods, we argue that the human action is a combination of meaningful body part actions. In detail, we divide human body into five parts: head, torso, arms, hands and legs. And for each of the body parts, we define several semantic body part actions, e.g., hand holding, hand waving. These semantic body part actions are strongly related to the body actions, e.g., writing, and jogging. Based on the idea, we propose a deep neural network based system: first, body parts are localized by a Semi-FCN network. Second, for each body parts, a Part Action Res-Net is used to predict semantic body part actions. And finally, we use SVM to fuse the body part actions and predict the entire body action. Experiments on two dataset: PASCAL VOC 2012 and Stanford-40 report mAP improvement from the state-of-the-art by 3.8% and 2.6% respectively.

Connected through hyperedge HE-1 (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-2 (induced by source paper "effective domain knowledge transfer with soft fine tuning"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "wasserstein cnn learning invariant features for nir vis face recognition"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-3 (induced by source paper "semantically interpretable activation maps what where how explanations within cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gradmask reduce overfitting by regularizing saliency"
    - "interpretability beyond classification output semantic bottleneck networks"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "causal learning and explanation of deep neural networks via autoencoded activations"
    - "methods for interpreting and understanding deep neural networks"
    - "semantic bottleneck for computer vision tasks"

Connected through hyperedge HE-4 (induced by source paper "story oriented image selection and placement"):
  with members:
    - "know2look commonsense knowledge for visual search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "yolo9000 better faster stronger"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "automatic description generation from images a survey of models datasets and evaluation measures"
    - "a hierarchical approach for generating descriptive image paragraphs"
    - "visual relationship detection with language priors"
    - "contextual action recognition with r cnn"

Connected through hyperedge HE-5 (induced by source paper "loss guided activation for action recognition in still images"):
  with members:
    - "detecting and recognizing human object interactions"
    - "expanded parts model for semantic description of humans in still images"
    - "attentional pooling for action recognition"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "contextual action recognition with r cnn"
    - "deep residual learning for image recognition"

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)