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: "human pose forecasting via deep markov models"
  Abstract: Human pose forecasting is an important problem in computer vision with applications to human-robot interaction, visual surveillance, and autonomous driving. Usually, forecasting algorithms use 3D skeleton sequences and are trained to forecast for a few milliseconds into the future. Long-range forecasting is challenging due to the difficulty of estimating how long a person continues an activity. To this end, our contributions are threefold: (i) we propose a generative framework for poses using variational autoencoders based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a pose-based action classifier, which we argue better reflects the subjective quality of pose forecasts than distance in coordinate space; (iii) last, for evaluation of the new model, we introduce a 480,000-frame video dataset called Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling and disassembling furniture. We demonstrate promising results for our approach on both Ikea FA and the existing NTU RGB+D dataset.

Connected through hyperedge HE-1 (induced by source paper "cater a diagnostic dataset for compositional actions and temporal reasoning"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "learning visual question answering by bootstrapping hard attention"
    - "distractor aware siamese networks for visual object tracking"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "modeling human motion with quaternion based neural networks"):
  with members:
    - "quaternion convolutional neural networks"
    - "a neural temporal model for human motion prediction"
    - "human motion modeling using dvgans"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "quaternet a quaternion based recurrent model for human motion"
    - "convolutional sequence to sequence model for human dynamics"
    - "quaternion convolutional neural networks for end to end automatic speech recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"

Connected through hyperedge HE-3 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"

Connected through hyperedge HE-4 (induced by source paper "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"):
  with members:
    - "pedx benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "densepose dense human pose estimation in the wild"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "coin a large scale dataset for comprehensive instructional video analysis"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "end to end dense video captioning with masked transformer"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "unsupervised visual linguistic reference resolution in instructional videos"
    - "diversity aware multi video summarization"
    - "action sets weakly supervised action segmentation without ordering constraints"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-6 (induced by source paper "action agnostic human pose forecasting"):
  with members:
    - "anticipation in human robot cooperation a recurrent neural network approach for multiple action sequences prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"
    - "learning human motion models for long term predictions"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "deep representation learning for human motion prediction and classification"
    - "forecasting human dynamics from static images"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "human action forecasting by learning task grammars"):
  with members:
    - "am i done predicting action progress in videos"
    - "spatiotemporal residual networks for video action recognition"
    - "unsupervised learning from narrated instruction videos"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)