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: "simultaneous feature and body part learning for real time robot awareness of human behaviors"
  Abstract: Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method to enable real-time robot awareness of human behaviors in practical robotics applications.

Connected through hyperedge HE-1 (induced by source paper "real time tracking by detection of human motion in rgb d camera networks"):
  with members:
    - "real time marker less multi person 3d pose estimation in rgb depth camera networks"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "real time marker less multi person 3d pose estimation in rgb depth camera networks"):
  with members:
    - "convolutional pose machines"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "human pose estimation with iterative error feedback"

Connected through hyperedge HE-3 (induced by source paper "space time representation of people based on 3d skeletal data a review"):
  with members:
    - "kernelized covariance for action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "linear time online action detection from 3d skeletal data using bags of gesturelets"
    - "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"
    - "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
    - "cross view action modeling learning and recognition"
    - "hopc histogram of oriented principal components of 3d pointclouds for action recognition"

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)