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: "holistic features for real time crowd behaviour anomaly detection"
  Abstract: This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness [1] and crowd conflict [2], with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abnormal training data is available we use a Support Vector Machine (SVM) for binary classification. We evaluate on two crowd behaviour anomaly detection datasets, achieving both state-of-the-art classification performance on the violent-flows dataset [3] as well as better than real-time processing performance (40 frames per second).

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 "loss switching fusion with similarity search for video classification"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "attention clusters purely attention based local feature integration for video classification"
    - "improved dense trajectory with cross streams"
    - "a survey on learning to hash"
    - "fast video classification via adaptive cascading of deep models"
    - "action recognition with trajectory pooled deep convolutional descriptors"

Connected through hyperedge HE-3 (induced by source paper "differential recurrent neural network and its application for human activity recognition"):
  with members:
    - "differential recurrent neural networks for action recognition"
    - "draw a recurrent neural network for image generation"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "translating videos to natural language using deep recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "deep differential recurrent neural networks"):
  with members:
    - "grid long short term memory"
    - "differential recurrent neural networks for action recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "translating videos to natural language using deep recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"
    - "multi dimensional recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"):
  with members:
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "fully convolutional crowd counting on highly congested scenes"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "detecting violent and abnormal crowd activity using temporal analysis of grey level co occurrence matrix glcm based texture measures"):
  with members:
    - "people counting in high density crowds from still images"

Connected through hyperedge HE-7 (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"
    - "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"

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)