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: "human action recognition without human"
  Abstract: The objective of this paper is to evaluate "human action recognition without human". Motion representation is frequently discussed in human action recognition. We have examined several sophisticated options, such as dense trajectories (DT) and the two-stream convolutional neural network (CNN). However, some features from the background could be too strong, as shown in some recent studies on human action recognition. Therefore, we considered whether a background sequence alone can classify human actions in current large-scale action datasets (e.g., UCF101). #R##N#In this paper, we propose a novel concept for human action analysis that is named "human action recognition without human". An experiment clearly shows the effect of a background sequence for understanding an action label.

Connected through hyperedge HE-1 (induced by source paper "why can t i dance in the mall learning to mitigate scene bias in action recognition"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "bias resilient neural network"
    - "long term feature banks for detailed video understanding"
    - "women also snowboard overcoming bias in captioning models"
    - "algorithms and theory for multiple source adaptation"
    - "dropblock a regularization method for convolutional networks"
    - "learning not to learn training deep neural networks with biased data"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"

Connected through hyperedge HE-2 (induced by source paper "cater a diagnostic dataset for compositional actions and temporal reasoning"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning visual question answering by bootstrapping hard attention"
    - "distractor aware siamese networks for visual object tracking"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "non local neural networks"

Connected through hyperedge HE-3 (induced by source paper "hallucinet ing spatiotemporal representations using 2d cnn"):
  with members:
    - "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "efficient video classification using fewer frames"
    - "temporal sequence distillation towards few frame action recognition in videos"
    - "motion feature network fixed motion filter for action recognition"
    - "rgb d based action recognition with light weight 3d convolutional networks"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "slowfast networks for video recognition"

Connected through hyperedge HE-4 (induced by source paper "skeleton based zero shot action recognition in joint pose language semantic space"):
  with members:
    - "a generative approach to zero shot and few shot action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "learning to compare relation network for few shot learning"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-5 (induced by source paper "differentiating objects by motion joint detection and tracking of small flying objects"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "re3 real time recurrent regression networks for visual tracking of generic objects"
    - "flow guided feature aggregation for video object detection"
    - "perceptual generative adversarial networks for small object detection"
    - "detect to track and track to detect"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "online multi target tracking using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "learning human pose models from synthesized data for robust rgb d action recognition"):
  with members:
    - "learning from synthetic humans"
    - "unsupervised learning of long term motion dynamics for videos"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "learning from simulated and unsupervised images through adversarial training"
    - "deep learning on lie groups for skeleton based action 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)