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 "learning multi modal self awareness models for autonomous vehicles from human driving"):
  Abstract: This paper presents a novel approach for learning self-awareness models for autonomous vehicles. The proposed technique is based on the availability of synchronized multi-sensor dynamic data related to different maneuvering tasks performed by a human operator. It is shown that different machine learning approaches can be used to first learn single modality models using coupled Dynamic Bayesian Networks; such models are then correlated at event level to discover contextual multi-modal concepts. In the presented case, visual perception and localization are used as modalities. Cross-correlations among modalities in time is discovered from data and are described as probabilistic links connecting shared and private multi-modal DBNs at the event (discrete) level. Results are presented on experiments performed on an autonomous vehicle, highlighting potentiality of the proposed approach to allow anomaly detection and autonomous decision making based on learned self-awareness models.

Includes members:
  Member "self paced deep learning for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low budget unsupervised label query through domain alignment enforcement")
  Member "a multi perspective approach to anomaly detection for self aware embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "abnormal event detection in videos using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "static force field representation of environments based on agents nonlinear motions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "a tube and droplet based approach for representing and analyzing motion trajectories":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "training adversarial discriminators for cross channel abnormal event detection in crowds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "plug and play cnn for crowd motion analysis an application in abnormal event detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")

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)