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: "r cnns for pose estimation and action detection"
  Abstract: We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images. Our approach involves training an R-CNN detector with loss functions depending on the task being tackled. We evaluate our method on the challenging PASCAL VOC dataset and compare it to previous leading approaches. Our method gives state-of-the-art results for keypoint and action prediction. Additionally, we introduce a new dataset for action detection, the task of simultaneously localizing people and classifying their actions, and present results using our approach.

Connected through hyperedge HE-1 (induced by source paper "mpm joint representation of motion and position map for cell tracking"):
  with members:
    - "joint multi frame detection and segmentation for multi cell tracking"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "microscopy cell segmentation via convolutional lstm networks"
    - "end to end representation learning for correlation filter based tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "poi multiple object tracking with high performance detection and appearance feature"

Connected through hyperedge HE-2 (induced by source paper "cross domain complementary learning with synthetic data for multi person part segmentation"):
  with members:
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "learning to adapt structured output space for semantic segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "densepose dense human pose estimation in the wild"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "human action recognition in drone videos using a few aerial training examples"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "real world anomaly detection in surveillance videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "human motion capture using a drone"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "intention recognition of pedestrians and cyclists by 2d pose estimation"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "an implementation of faster rcnn with study for region sampling"
    - "simple online and realtime tracking with a deep association metric"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-5 (induced by source paper "cross connected networks for multi task learning of detection and segmentation"):
  with members:
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "blitznet a real time deep network for scene understanding"
    - "learning to segment every thing"
    - "multi task self supervised visual learning"
    - "segflow joint learning for video object segmentation and optical flow"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "an overview of multi task learning in deep neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (induced by source paper "end to end multi task learning with attention"):
  with members:
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "progressive neural networks"
    - "incremental learning through deep adaptation"
    - "learning multiple visual domains with residual adapters"
    - "multi task self supervised visual learning"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "residual attention network for image classification"

Connected through hyperedge HE-7 (induced by source paper "pairwise body part attention for recognizing human object interactions"):
  with members:
    - "detecting and recognizing human object interactions"
    - "attentional pooling for action recognition"
    - "rmpe regional multi person pose estimation"
    - "image captioning with semantic attention"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "learning models for actions and person object interactions with transfer to question answering"
    - "contextual action recognition with r cnn"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "scale coding bag of deep features for human attribute and action recognition"):
  with members:
    - "scene recognition with cnns objects scales and dataset bias"
    - "bilinear cnn models for fine grained visual recognition"
    - "fuzzy human motion analysis"
    - "multi scale recognition with dag cnns"
    - "an expressive deep model for human action parsing from a single image"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"

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)