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: "beyond physical connections tree models in human pose estimation"
  Abstract: Simple tree models for articulated objects prevails in the last decade. However, it is also believed that these simple tree models are not capable of capturing large variations in many scenarios, such as human pose estimation. This paper attempts to address three questions: 1) are simple tree models sufficient? more specifically, 2) how to use tree models effectively in human pose estimation? and 3) how shall we use combined parts together with single parts efficiently? #R##N#Assuming we have a set of single parts and combined parts, and the goal is to estimate a joint distribution of their locations. We surprisingly find that no latent variables are introduced in the Leeds Sport Dataset (LSP) during learning latent trees for deformable model, which aims at approximating the joint distributions of body part locations using minimal tree structure. This suggests one can straightforwardly use a mixed representation of single and combined parts to approximate their joint distribution in a simple tree model. As such, one only needs to build Visual Categories of the combined parts, and then perform inference on the learned latent tree. Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset. Experiments on animal images from the VOC challenge further support our findings.

Connected through hyperedge HE-1 (induced by source paper "towards high performance human keypoint detection"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep learning for generic object detection a survey"
    - "simple baselines for human pose estimation and tracking"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "a lightweight blockchain based privacy protection for smart surveillance at the edge"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "a microservice enabled architecture for smart surveillance using blockchain technology"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "compositional human pose regression"

Connected through hyperedge HE-3 (induced by source paper "guaranteed scalable learning of latent tree models"):
  with members:
    - "fast detection of overlapping communities via online tensor methods"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "efficient active algorithms for hierarchical clustering"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-4 (induced by source paper "bi directional graph structure information model for multi person pose estimation"):
  with members:
    - "ai challenger a large scale dataset for going deeper in image understanding"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate multi person pose estimation in the wild"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "rmpe regional multi person pose estimation"
    - "object detection from video tubelets with convolutional neural networks"
    - "structured feature learning for pose estimation"

Connected through hyperedge HE-5 (induced by source paper "activity conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming"):
  with members:
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "continuous video to simple signals for swimming stroke detection with convolutional neural networks"
    - "extraction and classification of diving clips from continuous video footage"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "recurrent human pose estimation"
    - "chained predictions using convolutional neural networks"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-6 (induced by source paper "knowledge guided deep fractal neural networks for human pose estimation"):
  with members:
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recurrent human pose estimation"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "human pose estimation using deep consensus voting"
    - "chained predictions using convolutional neural networks"
    - "human pose estimation via convolutional part heatmap regression"

Connected through hyperedge HE-7 (induced by source paper "scalable latent tree model and its application to health analytics"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "tensor decompositions for learning latent variable models"
    - "efficient active algorithms for hierarchical clustering"
    - "spectral methods for learning multivariate latent tree structure"
    - "learning nonsingular phylogenies and hidden markov models"

Connected through hyperedge HE-8 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

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)