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: "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
  Abstract: In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification. To train and evaluate the proposed multi-objective technique, a new 100 image dataset referred to as Multi Task Crowd is constructed. This new dataset is the first computer vision dataset fully annotated for crowd counting, violent behaviour detection and density level classification. Our experiments show that a multi-task approach boosts individual task performance for all tasks and most notably for violent behaviour detection which receives a 9\% boost in ROC curve AUC (Area under the curve). The trained ResnetCrowd model is also evaluated on several additional benchmarks highlighting the superior generalisation of crowd analysis models trained for multiple objectives.

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"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-2 (induced by source paper "a survey of recent advances in cnn based single image crowd counting and density estimation"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "image de raining using a conditional generative adversarial network"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"

Connected through hyperedge HE-3 (induced by source paper "generating high quality crowd density maps using contextual pyramid cnns"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "spatiotemporal modeling for crowd counting in videos"
    - "understanding traffic density from large scale web camera data"
    - "multi style generative network for real time transfer"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "a survey of recent advances in cnn based single image crowd counting and density estimation"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"

Connected through hyperedge HE-4 (induced by source paper "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"):
  with members:
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "crowdnet a deep convolutional network for dense crowd counting"
    - "fully convolutional crowd counting on highly congested scenes"
    - "instance aware semantic segmentation via multi task network cascades"
    - "crowded scene analysis a survey"
    - "people counting in high density crowds from still images"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "fully convolutional neural networks for crowd segmentation"

Connected through hyperedge HE-5 (induced by source paper "plug and play cnn for crowd motion analysis an application in abnormal event detection"):
  with members:
    - "abnormal event detection in videos using generative adversarial nets"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "a discriminative framework for anomaly detection in large videos"
    - "emotion based crowd representation for abnormality detection"
    - "fully convolutional neural network for fast anomaly detection in crowded scenes"
    - "cnn aware binary map for general semantic segmentation"
    - "holistic features for real time crowd behaviour anomaly 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)