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: "emotion based crowd representation for abnormality detection"
  Abstract: In crowd behavior understanding, a model of crowd behavior need to be trained using the information extracted from video sequences. Since there is no ground-truth available in crowd datasets except the crowd behavior labels, most of the methods proposed so far are just based on low-level visual features. However, there is a huge semantic gap between low-level motion/appearance features and high-level concept of crowd behaviors. In this paper we propose an attribute-based strategy to alleviate this problem. While similar strategies have been recently adopted for object and action recognition, as far as we know, we are the first showing that the crowd emotions can be used as attributes for crowd behavior understanding. The main idea is to train a set of emotion-based classifiers, which can subsequently be used to represent the crowd motion. For this purpose, we collect a big dataset of video clips and provide them with both annotations of "crowd behaviors" and "crowd emotions". We show the results of the proposed method on our dataset, which demonstrate that the crowd emotions enable the construction of more descriptive models for crowd behaviors. We aim at publishing the dataset with the article, to be used as a benchmark for the communities.

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 "crowd video captioning"):
  with members:
    - "deep residual learning for image recognition"
    - "sequence to sequence video to text"
    - "tennisvid2text fine grained descriptions for domain specific videos"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "translating videos to natural language using deep recurrent neural networks"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "training adversarial discriminators for cross channel abnormal event detection in crowds"):
  with members:
    - "abnormal event detection in videos using generative adversarial nets"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "learning temporal regularity in video sequences"
    - "fully convolutional neural network for fast anomaly detection in crowded scenes"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (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"
    - "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "a discriminative framework for anomaly detection in large videos"
    - "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)