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: "transition forests learning discriminative temporal transitions for action recognition and detection"
  Abstract: A human action can be seen as transitions between one's body poses over time, where the transition depicts a temporal relation between two poses. Recognizing actions thus involves learning a classifier sensitive to these pose transitions as well as to static poses. In this paper, we introduce a novel method called transitions forests, an ensemble of decision trees that both learn to discriminate static poses and transitions between pairs of two independent frames. During training, node splitting is driven by alternating two criteria: the standard classification objective that maximizes the discrimination power in individual frames, and the proposed one in pairwise frame transitions. Growing the trees tends to group frames that have similar associated transitions and share same action label incorporating temporal information that was not available otherwise. Unlike conventional decision trees where the best split in a node is determined independently of other nodes, the transition forests try to find the best split of nodes jointly (within a layer) for incorporating distant node transitions. When inferring the class label of a new frame, it is passed down the trees and the prediction is made based on previous frame predictions and the current one in an efficient and online manner. We apply our method on varied skeleton action recognition and online detection datasets showing its suitability over several baselines and state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "semantics guided neural networks for efficient skeleton based human action recognition"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "eleatt rnn adding attentiveness to neurons in recurrent neural networks"
    - "deep high resolution representation learning for human pose estimation"
    - "learning deep bilinear transformation for fine grained image representation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-2 (induced by source paper "sparse coding of shape trajectories for facial expression and action recognition"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "a survey of automatic facial micro expression analysis databases methods and challenges"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "a deep learning perspective on the origin of facial expressions"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spontaneous subtle expression detection and recognition based on facial strain"

Connected through hyperedge HE-3 (induced by source paper "temporal transformer networks joint learning of invariant and discriminative time warping"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "can recurrent neural networks warp time"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "dynamic routing between capsules"
    - "learning invariant riemannian geometric representations using deep nets"
    - "graph based isometry invariant representation learning"
    - "a new representation of skeleton sequences for 3d action recognition"

Connected through hyperedge HE-4 (induced by source paper "optimized skeleton based action recognition via sparsified graph regression"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "sentence state lstm for text representation"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "cross sentence n ary relation extraction with graph lstms"
    - "conversation modeling on reddit using a graph structured lstm"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "category level 6d object pose recovery in depth images"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "kinematic layout aware random forests for depth based action recognition"

Connected through hyperedge HE-6 (induced by source paper "skeleton based gesture recognition using several fully connected layers with path signature features and temporal transformer module"):
  with members:
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "toward high performance online hccr"
    - "developing the path signature methodology and its application to landmark based human action recognition"
    - "convolutional pose machines"
    - "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"
    - "spatial transformer networks"
    - "improved deep convolutional neural network for online handwritten chinese character recognition using domain specific knowledge"

Connected through hyperedge HE-7 (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-8 (induced by source paper "first person hand action benchmark with rgb d videos and 3d hand pose annotations"):
  with members:
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "the 2017 hands in the million challenge on 3d hand pose estimation"
    - "real time hand tracking under occlusion from an egocentric rgb d sensor"
    - "depth based 3d hand pose estimation from current achievements to future goals"
    - "sembed semantic embedding of egocentric action videos"
    - "convolutional pose machines"

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)