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: "learning discriminative motion features through detection"
  Abstract: Despite huge success in the image domain, modern detection models such as Faster R-CNN have not been used nearly as much for video analysis. This is arguably due to the fact that detection models are designed to operate on single frames and as a result do not have a mechanism for learning motion representations directly from video. We propose a learning procedure that allows detection models such as Faster R-CNN to learn motion features directly from the RGB video data while being optimized with respect to a pose estimation task. Given a pair of video frames---Frame A and Frame B---we force our model to predict human pose in Frame A using the features from Frame B. We do so by leveraging deformable convolutions across space and time. Our network learns to spatially sample features from Frame B in order to maximize pose detection accuracy in Frame A. This naturally encourages our network to learn motion offsets encoding the spatial correspondences between the two frames. We refer to these motion offsets as DiMoFs (Discriminative Motion Features). #R##N#In our experiments we show that our training scheme helps learn effective motion cues, which can be used to estimate and localize salient human motion. Furthermore, we demonstrate that as a byproduct, our model also learns features that lead to improved pose detection in still-images, and better keypoint tracking. Finally, we show how to leverage our learned model for the tasks of spatiotemporal action localization and fine-grained action recognition.

Connected through hyperedge HE-1 (induced by source paper "gate shift networks for video action recognition"):
  with members:
    - "attentive spatio temporal representation learning for diving classification"
    - "video modeling with correlation networks"
    - "action recognition with spatial temporal discriminative filter banks"
    - "stm spatiotemporal and motion encoding for action recognition"
    - "epic fusion audio visual temporal binding for egocentric action recognition"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "grouped spatial temporal aggregation for efficient action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"

Connected through hyperedge HE-2 (induced by source paper "video modeling with correlation networks"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "end to end learning of motion representation for video understanding"
    - "motion feature network fixed motion filter for action recognition"
    - "using phase instead of optical flow for action recognition"
    - "videos as space time region graphs"
    - "spatio temporal channel correlation networks for action classification"
    - "representation flow for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-3 (induced by source paper "grouped spatial temporal aggregation for efficient action recognition"):
  with members:
    - "eco efficient convolutional network for online video understanding"
    - "long term feature banks for detailed video understanding"
    - "end to end learning of motion representation for video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "motion feature network fixed motion filter for action recognition"
    - "on the effectiveness of task granularity for transfer learning"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "videos as space time region graphs"

Connected through hyperedge HE-4 (induced by source paper "attentive spatio temporal representation learning for diving classification"):
  with members:
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "the kinetics human action video dataset"
    - "temporal relational reasoning in videos"
    - "posetrack joint multi person pose estimation and tracking"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "temporal segment networks towards good practices for deep action recognition"
    - "deep residual learning for image recognition"
    - "recurrent network models for human dynamics"

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)