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 multi perspective approach to anomaly detection for self aware embodied agents"
  Abstract: This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations. Both observation types are used to characterize agents' motion in a given environment. The proposed method generates locally uniform motion models by dividing a Gaussian process that approximates agents' displacements on the scene and provides a Shared Level (SL) self-awareness based on Environment Centered (EC) models. Such models are then used to train in a semi-unsupervised way a set of Generative Adversarial Networks (GANs) that produce an estimation of external and internal parameters of moving agents. Obtained results exemplify the feasibility of using multi-perspective data for predicting and analyzing trajectory information.

Connected through hyperedge HE-1 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "low shot learning from imaginary 3d model"
    - "self paced deep learning for weakly supervised object detection"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "experience replay for continual learning"
    - "hierarchy of gans for learning embodied self awareness model"
    - "foil it find one mismatch between image and language caption"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-2 (induced by source paper "hierarchy of gans for learning embodied self awareness model"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "learning multi modal self awareness models for autonomous vehicles from human driving"
    - "abnormal event detection in videos using generative adversarial nets"
    - "static force field representation of environments based on agents nonlinear motions"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "fully convolutional neural network for fast anomaly detection in crowded scenes"

Connected through hyperedge HE-3 (induced by source paper "learning multi modal self awareness models for autonomous vehicles from human driving"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "static force field representation of environments based on agents nonlinear motions"
    - "a tube and droplet based approach for representing and analyzing motion trajectories"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "image to image translation with conditional adversarial networks"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"

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)