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 comparative review of recent kinect based action recognition algorithms"
  Abstract: Video-based human action recognition is currently one of the most active research areas in computer vision. Various research studies indicate that the performance of action recognition is highly dependent on the type of features being extracted and how the actions are represented. Since the release of the Kinect camera, a large number of Kinect-based human action recognition techniques have been proposed in the literature. However, there still does not exist a thorough comparison of these Kinect-based techniques under the grouping of feature types, such as handcrafted versus deep learning features and depth-based versus skeleton-based features. In this paper, we analyze and compare ten recent Kinect-based algorithms for both cross-subject action recognition and cross-view action recognition using six benchmark datasets. In addition, we have implemented and improved some of these techniques and included their variants in the comparison. Our experiments show that the majority of methods perform better on cross-subject action recognition than cross-view action recognition, that skeleton-based features are more robust for cross-view recognition than depth-based features, and that deep learning features are suitable for large datasets.

Connected through hyperedge HE-1 (induced by source paper "a survey on 3d skeleton based action recognition using learning method"):
  with members:
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "richlt activated graph convolutional network for action recognition with incomplete skeletons"
    - "make skeleton based action recognition model smaller faster and better"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "skeleton image representation for 3d action recognition based on tree structure and reference joints"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"

Connected through hyperedge HE-2 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"):
  with members:
    - "loss switching fusion with similarity search for video classification"
    - "long term feature banks for detailed video understanding"
    - "learning discriminative video representations using adversarial perturbations"
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "a deeper look at power normalizations"
    - "non linear temporal subspace representations for activity recognition"
    - "higher order pooling of cnn features via kernel linearization for action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-3 (induced by source paper "take an emotion walk perceiving emotions from gaits using hierarchical attention pooling and affective mapping"):
  with members:
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "predicting 3d human dynamics from video"
    - "identifying emotions from walking using affective and deep features"
    - "pose guided human video generation"
    - "quaternet a quaternion based recurrent model for human motion"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-4 (induced by source paper "predict cluster unsupervised skeleton based action recognition"):
  with members:
    - "clustering and recognition of spatiotemporal features through interpretable embedding of sequence to sequence recurrent neural networks"
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "unsupervised learning of view invariant action representations"
    - "unsupervised learning of long term motion dynamics for videos"
    - "on human motion prediction using recurrent neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-5 (induced by source paper "coltrane convolutional trajectory network for deep map inference"):
  with members:
    - "cnn based action recognition and supervised domain adaptation on 3d body skeletons via kernel feature maps"
    - "road network fusion for incremental map updates"
    - "riobusdata outlier detection in bus routes of rio de janeiro"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "map a crowd sensing system for automatic map semantics identification"

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)