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: "fuzzy human motion analysis"
  Abstract: Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA. HighlightsA survey of fuzzy set oriented methods for human motion analysis is presented.This is the first time such a survey is presented in the fuzzy set literature.Categorization of existing approaches into three broad levels is performed.Insights and suggestions for future research are discussed.

Connected through hyperedge HE-1 (induced by source paper "i safe instant suspicious activity identification at the edge using fuzzy decision making"):
  with members:
    - "decentralized smart surveillance through microservices platform"
    - "blendmas a blockchain enabled decentralized microservices architecture for smart public safety"
    - "vstore a data store for analytics on large videos"
    - "real world anomaly detection in surveillance videos"
    - "focus querying large video datasets with low latency and low cost"
    - "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service"
    - "real time index authentication for event oriented surveillance video query using blockchain"
    - "real time human detection as an edge service enabled by a lightweight cnn"

Connected through hyperedge HE-2 (induced by source paper "scale coding bag of deep features for human attribute and action recognition"):
  with members:
    - "scene recognition with cnns objects scales and dataset bias"
    - "bilinear cnn models for fine grained visual recognition"
    - "multi scale recognition with dag cnns"
    - "an expressive deep model for human action parsing from a single image"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "r cnns for pose estimation and action detection"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "developing the path signature methodology and its application to landmark based human action recognition"):
  with members:
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "joint distance maps based action recognition with convolutional neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "rmpe regional multi person pose estimation"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"

Connected through hyperedge HE-4 (induced by source paper "rgb d based action recognition datasets a survey"):
  with members:
    - "3d human activity recognition with reconfigurable convolutional neural networks"
    - "cross view action modeling learning and recognition"
    - "learning human activities and object affordances from rgb d videos"

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)