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: "an expressive deep model for human action parsing from a single image"
  Abstract: This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "human pose estimation with spatial contextual information"):
  with members:
    - "path aggregation network for instance segmentation"
    - "situation recognition with graph neural networks"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "multi context attention for human pose estimation"
    - "mask r cnn"
    - "convolutional pose machines"

Connected through hyperedge HE-2 (induced by source paper "cascade feature aggregation for human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "quantized densely connected u nets for efficient landmark localization"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "multi scale structure aware network for human pose estimation"
    - "learning to refine human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"

Connected through hyperedge HE-3 (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-4 (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"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "r cnns for pose estimation and action detection"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "im2flow motion hallucination from static images for action recognition"):
  with members:
    - "deep view morphing"
    - "learning to generate long term future via hierarchical prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "temporal segment networks for action recognition in videos"
    - "actionvlad learning spatio temporal aggregation for action classification"
    - "forecasting human dynamics from static images"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "fast optical flow using dense inverse search"

Connected through hyperedge HE-6 (induced by source paper "single image action recognition by predicting space time saliency"):
  with members:
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "r cnns for pose estimation and action detection"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-7 (induced by source paper "human pose estimation from depth images via inference embedded multi task learning"):
  with members:
    - "cost effective active learning for deep image classification"
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "dari distance metric and representation integration for person verification"
    - "deep structured scene parsing by learning with image descriptions"
    - "geometric scene parsing with hierarchical lstm"
    - "a deep structured model with radius margin bound for 3d human activity recognition"
    - "structured feature learning for pose estimation"
    - "semantic object parsing with local global long short term memory"

Connected through hyperedge HE-8 (induced by source paper "structured feature learning for pose estimation"):
  with members:
    - "object detection from video tubelets with convolutional neural networks"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "conditional random fields as recurrent neural networks"
    - "human pose estimation with iterative error feedback"
    - "combining local appearance and holistic view dual source deep neural networks for human pose estimation"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "going deeper with convolutions"

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)