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 latent spatio temporal compositional model for human action recognition"
  Abstract: Action recognition is an important problem in multimedia understanding. This paper addresses this problem by building an expressive compositional action model. We model one action instance in the video with an ensemble of spatio-temporal compositions: a number of discrete temporal anchor frames, each of which is further decomposed to a layout of deformable parts. In this way, our model can identify a Spatio-Temporal And-Or Graph (STAOG) to represent the latent structure of actions \emph{e.g.} triple jumping, swinging and high jumping. The STAOG model comprises four layers: (i) a batch of leaf-nodes in bottom for detecting various action parts within video patches; (ii) the or-nodes over bottom, i.e. switch variables to activate their children leaf-nodes for structural variability; (iii) the and-nodes within an anchor frame for verifying spatial composition; and (iv) the root-node at top for aggregating scores over temporal anchor frames. Moreover, the contextual interactions are defined between leaf-nodes in both spatial and temporal domains. For model training, we develop a novel weakly supervised learning algorithm which iteratively determines the structural configuration (e.g. the production of leaf-nodes associated with the or-nodes) along with the optimization of multi-layer parameters. By fully exploiting spatio-temporal compositions and interactions, our approach handles well large intra-class action variance (\emph{e.g.} different views, individual appearances, spatio-temporal structures). The experimental results on the challenging databases demonstrate superior performance of our approach over other methods.

Connected through hyperedge HE-1 (induced by source paper "compositional temporal visual grounding of natural language event descriptions"):
  with members:
    - "wman weakly supervised moment alignment network for text based video segment retrieval"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "mac mining activity concepts for language based temporal localization"
    - "w talc weakly supervised temporal activity localization and classification"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "weakly supervised action localization by sparse temporal pooling network"
    - "tall temporal activity localization via language query"
    - "localizing moments in video with natural language"

Connected through hyperedge HE-2 (induced by source paper "aognets compositional grammatical architectures for deep learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "taking human out of learning applications a survey on automated machine learning"
    - "neural task planning with and or graph representations"
    - "cbam convolutional block attention module"
    - "squeezenext hardware aware neural network design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "network dissection quantifying interpretability of deep visual representations"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "human pose estimation from depth images via inference embedded multi task learning"):
  with members:
    - "cost effective active learning for deep image classification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "dari distance metric and representation integration for person verification"
    - "deep structured scene parsing by learning with image descriptions"
    - "geometric scene parsing with hierarchical lstm"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "structured feature learning for pose estimation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-4 (induced by source paper "order aware convolutional pooling for video based action recognition"):
  with members:
    - "discriminatively learned hierarchical rank pooling networks"
    - "rank pooling for action recognition"
    - "real time action recognition with enhanced motion vector cnns"
    - "temporal segment networks towards good practices for deep action recognition"
    - "long term temporal convolutions for action recognition"
    - "initialization strategies of spatio temporal convolutional neural networks"
    - "human action recognition using factorized spatio temporal convolutional networks"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-5 (induced by source paper "a deep structured model with radius margin bound for 3d human activity recognition"):
  with members:
    - "discriminatively trained and or graph models for object shape detection"
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "translating videos to natural language using deep recurrent neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "joint video and text parsing for understanding events and answering queries"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-6 (induced by source paper "temporal pyramid pooling based convolutional neural networks for action recognition"):
  with members:
    - "initialization strategies of spatio temporal convolutional neural networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "bag of visual words and fusion methods for action recognition comprehensive study and good practice"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "3d human activity recognition with reconfigurable convolutional neural networks"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "incorporating structural alternatives and sharing into hierarchy for multiclass object recognition and detection"
    - "joint video and text parsing for understanding events and answering queries"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-8 (induced by source paper "complex background subtraction by pursuing dynamic spatio temporal models"):
  with members:
    - "background modeling using adaptive pixelwise kernel variances in a hybrid feature space"
    - "integrating graph partitioning and matching for trajectory analysis in video surveillance"
    - "robust principal component analysis"

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)