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: "benchmarking and error diagnosis in multi instance pose estimation"
  Abstract: We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm's performance. Our technique is applied to compare the two leading methods for human pose estimation on the COCO Dataset, measure the sensitivity of pose estimation with respect to instance size, type and number of visible keypoints, clutter due to multiple instances, and the relative score of instances. The performance of algorithms, and the types of error they make, are highly dependent on all these variables, but mostly on the number of keypoints and the clutter. The analysis and software tools we propose offer a novel and insightful approach for understanding the behavior of pose estimation algorithms and an effective method for measuring their strengths and weaknesses.

Connected through hyperedge HE-1 (induced by source paper "arbee towards automated recognition of bodily expression of emotion in the wild"):
  with members:
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"
    - "simple online and realtime tracking"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "deepfakes for medical video de identification privacy protection and diagnostic information preservation"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "live face de identification in video"
    - "vision based gait analysis for senior care"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "exploiting offset guided network for pose estimation and tracking"
    - "pose flow efficient online pose tracking"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "joint flow temporal flow fields for multi person tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "attention guided unified network for panoptic segmentation"

Connected through hyperedge HE-4 (induced by source paper "pose neural fabrics search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "exploring randomly wired neural networks for image recognition"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "objects as points"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "regularized evolution for image classifier architecture search"

Connected through hyperedge HE-5 (induced by source paper "multi scale aggregation r cnn for 2d multi person pose estimation"):
  with members:
    - "posefix model agnostic general human pose refinement network"
    - "simple baselines for human pose estimation and tracking"
    - "multiposenet fast multi person pose estimation using pose residual network"
    - "megdet a large mini batch object detector"
    - "towards accurate multi person pose estimation in the wild"
    - "cascaded pyramid network for multi person pose estimation"
    - "multi context attention for human pose estimation"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-6 (induced by source paper "anchor loss modulating loss scale based on prediction difficulty"):
  with members:
    - "multi scale structure aware network for human pose estimation"
    - "smooth loss functions for deep top k classification"
    - "learning to reweight examples for robust deep learning"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "focal loss for dense object detection"
    - "a general and adaptive robust loss function"

Connected through hyperedge HE-7 (induced by source paper "amil adversarial multi instance learning for human pose estimation"):
  with members:
    - "semi and weakly supervised human pose estimation"
    - "attention based deep multiple instance learning"
    - "densepose dense human pose estimation in the wild"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "dynamic routing between capsules"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "tracklinic diagnosis of challenge factors in visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "skimming perusal tracking a framework for real time and robust long term tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"

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)