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: "expanded parts model for semantic description of humans in still images"
  Abstract: We introduce an Expanded Parts Model (EPM) for recognizing human attributes (e.g., young, short hair, wearing suits) and actions (e.g., running, jumping) in still images. An EPM is a collection of part templates which are learnt discriminatively to explain specific scale-space regions in the images (in human centric coordinates). This is in contrast to current models which consist of a relatively few (i.e., a mixture of) ‘average’ templates. EPM uses only a subset of the parts to score an image and scores the image sparsely in space, i.e., it ignores redundant and random background in an image. To learn our model, we propose an algorithm which automatically mines parts and learns corresponding discriminative templates together with their respective locations from a large number of candidate parts. We validate our method on three recent challenging datasets of human attributes and actions. We obtain convincing qualitative and state-of-the-art quantitative results on the three datasets.

Connected through hyperedge HE-1 (induced by source paper "zero shot object detection"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"
    - "semantic autoencoder for zero shot learning"
    - "discriminative bimodal networks for visual localization and detection with natural language queries"
    - "recent advances in zero shot recognition"
    - "visually aligned word embeddings for improving zero shot learning"

Connected through hyperedge HE-2 (induced by source paper "loss guided activation for action recognition in still images"):
  with members:
    - "detecting and recognizing human object interactions"
    - "attentional pooling for action recognition"
    - "single image action recognition using semantic body part actions"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "discriminatively trained latent ordinal model for video classification"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "unsupervised representation learning by sorting sequences"
    - "lomo latent ordinal model for facial analysis in videos"
    - "convolutional two stream network fusion for video action recognition"
    - "adascan adaptive scan pooling in deep convolutional neural networks for human action recognition in videos"
    - "videolstm convolves attends and flows for action recognition"
    - "self supervised video representation learning with odd one out networks"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-4 (induced by source paper "robust automated human activity recognition and its application to sleep research"):
  with members:
    - "rank pooling for action recognition"
    - "an end to end generative framework for video segmentation and recognition"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "a robust and efficient video representation for action recognition"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional 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)