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: "3d human pose estimation in rgbd images for robotic task learning"
  Abstract: We propose an approach to estimate 3D human pose in real world units from a single RGBD image and show that it exceeds performance of monocular 3D pose estimation approaches from color as well as pose estimation exclusively from depth. Our approach builds on robust human keypoint detectors for color images and incorporates depth for lifting into 3D. We combine the system with our learning from demonstration framework to instruct a service robot without the need of markers. Experiments in real world settings demonstrate that our approach enables a PR2 robot to imitate manipulation actions observed from a human teacher.

Connected through hyperedge HE-1 (induced by source paper "metric scale truncation robust heatmaps for 3d human pose estimation"):
  with members:
    - "in the wild human pose estimation using explicit 2d features and intermediate 3d representations"
    - "ordinal depth supervision for 3d human pose estimation"
    - "multi scale structure aware network for human pose estimation"
    - "yolov3 an incremental improvement"
    - "video based reconstruction of 3d people models"
    - "2d 3d pose estimation and action recognition using multitask deep learning"
    - "how robust is 3d human pose estimation to occlusion"
    - "3d human pose estimation in the wild by adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "towards robust rgb d human mesh recovery"):
  with members:
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "learning elementary structures for 3d shape generation and matching"
    - "neural scene decomposition for multi person motion capture"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "weakly supervised discovery of geometry aware representation for 3d human pose estimation"
    - "shape aware human pose and shape reconstruction using multi view images"
    - "ordinal depth supervision for 3d human pose estimation"
    - "vision based pose estimation for augmented reality a comparison study"

Connected through hyperedge HE-3 (induced by source paper "bio lstm a biomechanically inspired recurrent neural network for 3 d pedestrian pose and gait prediction"):
  with members:
    - "pedx benchmark dataset for metric 3d pose estimation of pedestrians in complex urban intersections"
    - "quaternet a quaternion based recurrent model for human motion"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "densepose dense human pose estimation in the wild"
    - "decomposing motion and content for natural video sequence prediction"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-4 (induced by source paper "teaching robots to imitate a human with no on teacher sensors what are the key challenges"):
  with members:
    - "rolling shutter camera synchronization with sub millisecond accuracy"
    - "bop benchmark for 6d object pose estimation"
    - "on the two view geometry of unsynchronized cameras"
    - "towards accurate multi person pose estimation in the wild"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-5 (induced by source paper "two stage transfer learning for heterogeneous robot detection and 3d joint position estimation in a 2d camera image using cnn"):
  with members:
    - "multi objective convolutional neural networks for robot localisation and 3d position estimation in 2d camera images"
    - "transfer learning for unseen robot detection and joint estimation on a multi objective convolutional neural network"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "multi 3d camera mapping for predictive and reflexive robot manipulator trajectory estimation"
    - "automatic calibration of a robot manipulator and multi 3d camera system"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-6 (induced by source paper "combined task and action learning from human demonstrations for mobile manipulation applications"):
  with members:
    - "do what i want not what i did imitation of skills by planning sequences of actions"
    - "learning symbolic models of stochastic domains"

Connected through hyperedge HE-7 (induced by source paper "a deep learning approach for multi view engagement estimation of children in a child robot joint attention task"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "yolov3 an incremental improvement"
    - "personalized machine learning for robot perception of affect and engagement in autism therapy"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "hand keypoint detection in single images using multiview bootstrapping"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "towards engagement models that consider individual factors in hri on the relation of extroversion and negative attitude towards robots to gaze and speech during a human robot assembly task"

Connected through hyperedge HE-8 (induced by source paper "lstm based network for human gait stability prediction in an intelligent robotic rollator"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "convolutional pose machines"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)