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: "going deeper into action recognition a survey"
  Abstract: Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader.

Connected through hyperedge HE-1 (induced by source paper "multi task deep learning for real time 3d human pose estimation and action recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "3d human pose estimation in the wild by adversarial learning"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-2 (induced by source paper "turbo learning framework for human object interactions recognition and human pose estimation"):
  with members:
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "detecting and recognizing human object interactions"
    - "human pose regression by combining indirect part detection and contextual information"
    - "learning to detect human object interactions"
    - "knowledge guided deep fractal neural networks for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"
    - "rmpe regional multi person pose estimation"

Connected through hyperedge HE-3 (induced by source paper "2d 3d pose estimation and action recognition using multitask deep learning"):
  with members:
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "monocap monocular human motion capture using a cnn coupled with a geometric prior"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "human pose regression by combining indirect part detection and contextual information"
    - "body joint guided 3d deep convolutional descriptors for action recognition"

Connected through hyperedge HE-4 (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"
    - "large scale continuous gesture recognition using convolutional neural networks"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "differential recurrent neural networks for action recognition"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-5 (induced by source paper "real time action recognition with dissimilarity based training of specialized module networks"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "convolutional two stream network fusion for video action recognition"
    - "deep residual learning for image recognition"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-6 (induced by source paper "a survey of content aware video analysis for sports"):
  with members:
    - "applying deep learning to basketball trajectories"
    - "a robust and efficient video representation for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "every moment counts dense detailed labeling of actions in complex videos"
    - "action recognition with trajectory pooled deep convolutional descriptors"
    - "translating videos to natural language using deep recurrent neural networks"
    - "reading text in the wild with convolutional neural networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "interpretable 3d human action analysis with temporal convolutional networks"):
  with members:
    - "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"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "temporal convolutional networks for action segmentation and detection"
    - "identity mappings in deep residual networks"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-8 (induced by source paper "probabilistic complex event recognition a survey"):
  with members:
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "a probabilistic logic programming event calculus"
    - "lifted relational neural networks"
    - "complex events recognition under uncertainty in a sensor network"
    - "relational bayesian networks"
    - "location based reasoning about complex multi agent behavior"
    - "a model for reasoning with uncertain rules in event composition systems"
    - "probabilistic event calculus for event 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)