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 pose based attention draws focus to hands"
  Abstract: We propose a new spatio-temporal attention based mechanism for human action recognition able to automatically attend to the hands most involved into the studied action and detect the most discriminative moments in an action. Attention is handled in a recurrent manner employing Recurrent Neural Network (RNN) and is fully-differentiable. In contrast to standard soft-attention based mechanisms, our approach does not use the hidden RNN state as input to the attention model. Instead, attention distributions are extracted using external information: human articulated pose. We performed an extensive ablation study to show the strengths of this approach and we particularly studied the conditioning aspect of the attention mechanism. We evaluate the method on the largest currently available human action recognition dataset, NTU-RGB+D, and report state-of-the-art results. Other advantages of our model are certain aspects of explanability, as the spatial and temporal attention distributions at test time allow to study and verify on which parts of the input data the method focuses.

Connected through hyperedge HE-1 (induced by source paper "an attention enhanced graph convolutional lstm network for skeleton based action recognition"):
  with members:
    - "part based graph convolutional network for action recognition"
    - "action recognition with spatio temporal visual attention on skeleton image sequences"
    - "memory attention networks for skeleton based action recognition"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "learning human object interactions by graph parsing neural networks"
    - "how powerful are graph neural networks"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "situation recognition with graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "egocentric hand track and object based human action recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "left right hand segmentation in egocentric videos"
    - "first person action object detection with egonet"
    - "simple online and realtime tracking"

Connected through hyperedge HE-3 (induced by source paper "video action transformer network"):
  with members:
    - "image transformer"
    - "videocapsulenet a simplified network for action detection"
    - "classifying collisions with spatio temporal action graph networks"
    - "yh technologies at activitynet challenge 2018"
    - "actor centric relation network"
    - "videos as space time region graphs"
    - "object level visual reasoning in videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-4 (induced by source paper "glimpse clouds human activity recognition from unstructured feature points"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "structured attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "pose conditioned spatio temporal attention for human action recognition"
    - "lattice long short term memory for human action recognition"
    - "learning video object segmentation with visual memory"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"

Connected through hyperedge HE-5 (induced by source paper "deepgru deep gesture recognition utility"):
  with members:
    - "exploiting recurrent neural networks and leap motion controller for the recognition of sign language and semaphoric hand gestures"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "non linear temporal subspace representations for activity recognition"
    - "sts classification with dual stream cnn"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "excitation backprop for rnns"

Connected through hyperedge HE-6 (induced by source paper "deep temporal lstm for daily living action recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "convolutional two stream network fusion for video action recognition"
    - "p cnn pose based cnn features for 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)