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: "fully convolutional neural network for fast anomaly detection in crowded scenes"
  Abstract: We present an efficient method for detecting and localizing anomalies in videos showing crowded scenes. Research on {\it fully convolutional neural networks} (FCNs) has shown the potentials of this technology for object detection and localization, especially in images. We investigate how to involve temporal data, and how to transform a supervised FCN into an unsupervised one such that the resulting FCN ensures anomaly detection. Altogether, we propose an FCN-based architecture for anomaly detection and localization in crowded scenes videos. For reducing computations and, consequently, improving performance both with respect to speed and accuracy, we investigate the use of cascaded out-layer detection. Our architecture includes two main components, one for feature representation, and one for cascaded out-layer detection. Experimental results on Subway and UCSD benchmarks confirm that the detection and localization accuracy of our method is comparable to state-of-the-art methods, but at a significantly increased speed of 370 fps.

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 "fast distance based anomaly detection in images using an inception like autoencoder"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "anomaly detection using deep learning based image completion"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "billion scale similarity search with gpus"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "fault diagnosing slam for varying scale change detection"):
  with members:
    - "ibow lcd an appearance based loop closure detection approach using incremental bags of binary words"
    - "multi attention recurrent network for human communication comprehension"
    - "map management for efficient long term visual localization in outdoor environments"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "detection by localization maintenance free change object detector"
    - "lightweight unsupervised deep loop closure"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "street scene a new dataset and evaluation protocol for video anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "energy based models for video anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "challenges in time stamp aware anomaly detection in traffic videos"):
  with members:
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "learning temporal regularity in video sequences"
    - "context aware cnns for person head detection"
    - "flownet learning optical flow with convolutional networks"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "very deep convolutional networks for large scale image recognition"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-6 (induced by source paper "place specific background modeling using recursive autoencoders"):
  with members:
    - "towards image understanding from deep compression without decoding"
    - "end to end optimized image compression"
    - "yolo9000 better faster stronger"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-7 (induced by source paper "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder"):
  with members:
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "ordered pooling of optical flow sequences for action recognition"
    - "a discriminative framework for anomaly detection in large videos"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "learning deep representations of appearance and motion for anomalous event detection"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-8 (induced by source paper "hierarchy of gans for learning embodied self awareness model"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "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"

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)