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: "modeep a deep learning framework using motion features for human pose estimation"
  Abstract: In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We apply our architecture to this dataset and report significantly better performance than current state-of-the-art pose detection systems.

Connected through hyperedge HE-1 (induced by source paper "unipose unified human pose estimation in single images and videos"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "cascade feature aggregation for human pose estimation"
    - "human pose estimation with spatial contextual information"
    - "deep high resolution representation learning for human pose estimation"
    - "lighttrack a generic framework for online top down human pose tracking"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "multi scale structure aware network for human pose estimation"

Connected through hyperedge HE-2 (induced by source paper "deep learning tools for the measurement of animal behavior in neuroscience"):
  with members:
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "pretraining boosts out of domain robustness for pose estimation"
    - "expressive body capture 3d hands face and body from a single image"
    - "pifpaf composite fields for human pose estimation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "deep high resolution representation learning for human pose estimation"
    - "detection and tracking of multiple mice using part proposal networks"
    - "human pose estimation for real world crowded scenarios"

Connected through hyperedge HE-3 (induced by source paper "person in wifi fine grained person perception using wifi"):
  with members:
    - "joint activity recognition and indoor localization with wifi fingerprints"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "csi net unified human body characterization and action recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "simple baselines for human pose estimation and tracking"
    - "efficient online multi person 2d pose tracking with recurrent spatio temporal affinity fields"
    - "characterization of a rs lidar for 3d perception"
    - "combining lidar space clustering and convolutional neural networks for pedestrian detection"

Connected through hyperedge HE-4 (induced by source paper "object parsing in sequences using coordconv gated recurrent networks"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "instance segmentation and tracking with cosine embeddings and recurrent hourglass networks"
    - "thin slicing network a deep structured model for pose estimation in videos"
    - "lstm pose machines"
    - "recurrent 3d pose sequence machines"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "warpnet weakly supervised matching for single view reconstruction"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning to detect and track visible and occluded body joints in a virtual world"):
  with members:
    - "simple online and realtime tracking with a deep association metric"
    - "detect to track and track to detect"
    - "posetrack a benchmark for human pose estimation and tracking"
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "posetrack joint multi person pose estimation and tracking"

Connected through hyperedge HE-7 (induced by source paper "deep multimodal subspace clustering networks"):
  with members:
    - "efficient large scale multi modal classification"
    - "deep subspace clustering networks"
    - "a deep metric for multimodal registration"
    - "multimodal residual learning for visual qa"
    - "convolutional two stream network fusion for video action recognition"
    - "tensorflow a system for large scale machine learning"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "emonets multimodal deep learning approaches for emotion recognition in video"

Connected through hyperedge HE-8 (induced by source paper "human pose and path estimation from aerial video using dynamic classifier selection"):
  with members:
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "learning a deep model for human action recognition from novel viewpoints"
    - "applying deep learning to basketball trajectories"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "how transferable are features in deep neural networks"
    - "solving multiclass learning problems via error correcting output codes"

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)