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: "a discussion on the validation tests employed to compare human action recognition methods using the msr action3d dataset"
  Abstract: This paper aims to determine which is the best human action recognition method based on features extracted from RGB-D devices, such as the Microsoft Kinect. A review of all the papers that make reference to MSR Action3D, the most used dataset that includes depth information acquired from a RGB-D device, has been performed. We found that the validation method used by each work differs from the others. So, a direct comparison among works cannot be made. However, almost all the works present their results comparing them without taking into account this issue. Therefore, we present different rankings according to the methodology used for the validation in orden to clarify the existing confusion.

Connected through hyperedge HE-1 (induced by source paper "multi view region adaptive multi temporal dmm and rgb action recognition"):
  with members:
    - "action recognition for depth video using multi view dynamic images"
    - "two stream 3d convolutional neural network for skeleton based action recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "towards good practices for very deep two stream convnets"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "cross view action modeling learning and recognition"

Connected through hyperedge HE-2 (induced by source paper "robust 3d action recognition through sampling local appearances and global distributions"):
  with members:
    - "elastic functional coding of riemannian trajectories"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "differential recurrent neural networks for action recognition"
    - "histogram of oriented principal components for cross view action recognition"
    - "mining mid level features for action recognition based on effective skeleton representation"
    - "learning human activities and object affordances from rgb d videos"
    - "unstructured human activity detection from rgbd images"

Connected through hyperedge HE-3 (induced by source paper "fast gesture recognition with multiple stream discrete hmms on 3d skeletons"):
  with members:
    - "deep convolutional neural networks for action recognition using depth map sequences"

Connected through hyperedge HE-4 (induced by source paper "space time representation of people based on 3d skeletal data a review"):
  with members:
    - "simultaneous feature and body part learning for real time robot awareness of human behaviors"
    - "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"
    - "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)