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: "a hierarchical pose based approach to complex action understanding using dictionaries of actionlets and motion poselets"
  Abstract: In this paper, we introduce a new hierarchical model for human action recognition using body joint locations. Our model can categorize complex actions in videos, and perform spatio-temporal annotations of the atomic actions that compose the complex action being performed.That is, for each atomic action, the model generates temporal action annotations by estimating its starting and ending times, as well as, spatial annotations by inferring the human body parts that are involved in executing the action. our model includes three key novel properties: (i) it can be trained with no spatial supervision, as it can automatically discover active body parts from temporal action annotations only; (ii) it jointly learns flexible representations for motion poselets and actionlets that encode the visual variability of body parts and atomic actions; (iii) a mechanism to discard idle or non-informative body parts which increases its robustness to common pose estimation errors. We evaluate the performance of our method using multiple action recognition benchmarks. Our model consistently outperforms baselines and state-of-the-art action recognition methods.

Connected through hyperedge HE-1 (induced by source paper "multi modal three stream network for action recognition"):
  with members:
    - "pose conditioned spatio temporal attention for human action recognition"
    - "convolutional pose machines"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "pose for action action for pose"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "p cnn pose based cnn features for action recognition"

Connected through hyperedge HE-2 (induced by source paper "skeleton based human action recognition with global context aware attention lstm networks"):
  with members:
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "online human action detection using joint classification regression recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "pose based two stream relational networks for action recognition in videos"):
  with members:
    - "recurrent relational networks for complex relational reasoning"
    - "attention is all you need"
    - "towards accurate multi person pose estimation in the wild"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "stacked hourglass networks for human pose estimation"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "pose for action action for pose"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic semi supervised approach to multi task human activity modeling"):
  with members:
    - "disentangled sequential autoencoder"
    - "a structured variational autoencoder for contextual morphological inflection"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "advances in variational inference"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-5 (induced by source paper "skeleton based action recognition using spatio temporal lstm network with trust gates"):
  with members:
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "rank pooling for action recognition"
    - "unsupervised learning of long term motion dynamics for videos"
    - "cern confidence energy recurrent network for group activity recognition"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-6 (induced by source paper "chained multi stream networks exploiting pose motion and appearance for action classification and detection"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "improved dense trajectory with cross streams"
    - "orientation boosted voxel nets for 3d object recognition"
    - "actionness estimation using hybrid fully convolutional networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "sympathy for the details dense trajectories and hybrid classification architectures for action recognition"

Connected through hyperedge HE-7 (induced by source paper "discovery and recognition of motion primitives in human activities"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "activitynet challenge 2017 summary"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"
    - "learning from synthetic humans"
    - "learning temporal regularity in video sequences"
    - "sparseness meets deepness 3d human pose estimation from monocular video"
    - "direct prediction of 3d body poses from motion compensated sequences"

Connected through hyperedge HE-8 (induced by source paper "spatio temporal lstm with trust gates for 3d human action recognition"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "online human action detection using joint classification regression recurrent neural networks"
    - "deep multimodal feature analysis for action recognition in rgb d videos"
    - "a siamese long short term memory architecture for human re identification"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "space time representation of people based on 3d skeletal data a review"

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)