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: "anomaly detection in video using predictive convolutional long short term memory networks"
  Abstract: Automating the detection of anomalous events within long video sequences is challenging due to the ambiguity of how such events are defined. We approach the problem by learning generative models that can identify anomalies in videos using limited supervision. We propose end-to-end trainable composite Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the evolution of a video sequence from a small number of input frames. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores as they diverge further from the actual sequence over time. The models utilize a composite structure and examine the effects of conditioning in learning more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracy. The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on anomaly detection datasets. Conv-LSTM units are shown to be an effective tool for modeling and predicting video sequences.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in road traffic using visual surveillance a survey"):
  with members:
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "real world anomaly detection in surveillance videos"
    - "review on computer vision techniques in emergency situations"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "video captioning via hierarchical reinforcement learning"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-2 (induced by source paper "unsupervised traffic accident detection in first person videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "classifying collisions with spatio temporal action graph networks"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"

Connected through hyperedge HE-3 (induced by source paper "graph embedded pose clustering for anomaly detection"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "pose flow efficient online pose tracking"
    - "deep clustering for unsupervised learning of visual features"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "latent space autoregression for novelty detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "robust lane detection from continuous driving scenes using deep neural networks"):
  with members:
    - "a deep spatio temporal fuzzy neural network for passenger demand prediction"
    - "rethinking imagenet pre training"
    - "privacy preserving collaborative deep learning with unreliable participants"
    - "towards end to end lane detection an instance segmentation approach"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "youtube vos sequence to sequence video object segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "inception inspired lstm for next frame video prediction"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "lstm a search space odyssey"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "wide residual networks"
    - "end to end learning of driving models from large scale video datasets"
    - "learning temporal transformations from time lapse videos"

Connected through hyperedge HE-6 (induced by source paper "a promotion method for generation error based video anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder"
    - "adversarially learned one class classifier for novelty detection"
    - "avid adversarial visual irregularity detection"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "what goes around comes around cycle consistency based short term motion prediction for anomaly detection using generative adversarial networks"):
  with members:
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "future frame prediction for anomaly detection a new baseline"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "vizads b analyzing sequences of ads b images using explainable convolutional lstm encoder decoder to detect cyber attacks"):
  with members:
    - "explaining anomalies detected by autoencoders using shap"
    - "gee a gradient based explainable variational autoencoder for network anomaly detection"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "using lstm encoder decoder algorithm for detecting anomalous ads b messages"
    - "unsupervised learning for physical interaction through video prediction"
    - "why should i trust you explaining the predictions of any classifier"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)