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: "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
  Abstract: Skeleton-based human action recognition has recently drawn increasing attentions with the availability of large-scale skeleton datasets. The most crucial factors for this task lie in two aspects: the intra-frame representation for joint co-occurrences and the inter-frame representation for skeletons' temporal evolutions. In this paper we propose an end-to-end convolutional co-occurrence feature learning framework. The co-occurrence features are learned with a hierarchical methodology, in which different levels of contextual information are aggregated gradually. Firstly point-level information of each joint is encoded independently. Then they are assembled into semantic representation in both spatial and temporal domains. Specifically, we introduce a global spatial aggregation scheme, which is able to learn superior joint co-occurrence features over local aggregation. Besides, raw skeleton coordinates as well as their temporal difference are integrated with a two-stream paradigm. Experiments show that our approach consistently outperforms other state-of-the-arts on action recognition and detection benchmarks like NTU RGB+D, SBU Kinect Interaction and PKU-MMD.

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"
    - "a comparative review of recent kinect based action recognition algorithms"
    - "skelemotion a new representation of skeleton joint sequences based on motion information for 3d action recognition"

Connected through hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"

Connected through hyperedge HE-3 (induced by source paper "a deep learning framework for assessing physical rehabilitation exercises"):
  with members:
    - "a fine to coarse convolutional neural network for 3d human action recognition"
    - "dynamic temporal pyramid network a closer look at multi scale modeling for activity detection"
    - "temporal action detection with structured segment networks"
    - "deep representation learning for human motion prediction and classification"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "exercise motion classification from large scale wearable sensor data using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "etri activity3d a large scale rgb d dataset for robots to recognize daily activities of the elderly"):
  with members:
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "glimpse clouds human activity recognition from unstructured feature points"
    - "ensemble one dimensional convolution neural networks for skeleton based action recognition"
    - "neural networks with activation networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "two stream rnn cnn for action recognition in 3d videos"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-5 (induced by source paper "skeleton based action recognition using a stacked denoising autoencoder with constraints of privileged information"):
  with members:
    - "skeleton based action recognition with spatial reasoning and temporal stack learning"
    - "interpretable 3d human action analysis with temporal convolutional networks"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "ssd single shot multibox detector"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "deephums deep human motion signature for 3d skeletal sequences"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "humanmeshnet polygonal mesh recovery of humans"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "deep textured 3d reconstruction of human bodies"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"

Connected through hyperedge HE-7 (induced by source paper "deep independently recurrent neural network indrnn"):
  with members:
    - "temporal transformer networks joint learning of invariant and discriminative time warping"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "part based graph convolutional network for action recognition"
    - "learning long term dependencies via fourier recurrent units"
    - "view adaptive neural networks for high performance skeleton based human action recognition"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-8 (induced by source paper "chirality nets for human pose regression"):
  with members:
    - "pic permutation invariant critic for multi agent deep reinforcement learning"
    - "unsupervised learning of object structure and dynamics from videos"
    - "on the convergence of adam and beyond"
    - "ordinal depth supervision for 3d human pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "action agnostic human pose forecasting"

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)