human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graph embedded pose clustering for anomaly detection"):
  Abstract: We propose a new method for anomaly detection of human actions. Our method works directly on human pose graphs that can be computed from an input video sequence. This makes the analysis independent of nuisance parameters such as viewpoint or illumination. We map these graphs to a latent space and cluster them. Each action is then represented by its soft-assignment to each of the clusters. This gives a kind of "bag of words" representation to the data, where every action is represented by its similarity to a group of base action-words. Then, we use a Dirichlet process based mixture, that is useful for handling proportional data such as our soft-assignment vectors, to determine if an action is normal or not. #R##N#We evaluate our method on two types of data sets. The first is a fine-grained anomaly detection data set (e.g. ShanghaiTech) where we wish to detect unusual variations of some action. The second is a coarse-grained anomaly detection data set (e.g.,\ a Kinetics-based data set) where few actions are considered normal, and every other action should be considered abnormal. #R##N#Extensive experiments on the benchmarks show that our method performs considerably better than other state of the art methods.

Includes members:
  Member "learning regularity in skeleton trajectories for anomaly detection in videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d resnet with ranking loss function for abnormal activity detection in videos")
  Member "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging")
  Member "pose flow efficient online pose tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "deep clustering for unsupervised learning of visual features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "latent space autoregression for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "abnormal event detection in videos using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)