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: "large scale continuous gesture recognition using convolutional neural networks"
  Abstract: This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked $3^{rd}$ place in this challenge.

Connected through hyperedge HE-1 (induced by source paper "chalearn looking at people isogd and congd large scale rgb d gesture recognition"):
  with members:
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "rank pooling for action recognition"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "automatic recognition of facial displays of unfelt emotions"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "progression modelling for online and early gesture detection"):
  with members:
    - "encouraging lstms to anticipate actions very early"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "beyond temporal pooling recurrence and temporal convolutions for gesture recognition in video"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "efficient object localization using convolutional networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "moddrop adaptive multi modal gesture recognition"

Connected through hyperedge HE-3 (induced by source paper "depth pooling based large scale 3d action recognition with convolutional neural networks"):
  with members:
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "going deeper into action recognition a survey"
    - "differential recurrent neural networks for action recognition"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-4 (induced by source paper "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"):
  with members:
    - "large scale isolated gesture recognition using convolutional neural networks"
    - "rank pooling for action recognition"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "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"
    - "rgb d based action recognition datasets a survey"

Connected through hyperedge HE-5 (induced by source paper "large scale isolated gesture recognition using convolutional neural networks"):
  with members:
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "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"
    - "beyond short snippets deep networks for video classification"
    - "differential recurrent neural networks for action recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "mining mid level features for action recognition based on effective skeleton representation"

Connected through hyperedge HE-6 (induced by source paper "chalearn looking at people a review of events and resources"):
  with members:
    - "object scene convolutional neural networks for event recognition in images"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "moddrop adaptive multi modal gesture recognition"
    - "one shot learning gesture recognition using hog hof features"

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)