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: "autoencoder with recurrent neural networks for video forgery detection"
  Abstract: Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.

Connected through hyperedge HE-1 (induced by source paper "improving the security of image manipulation detection through one and a half class multiple classification"):
  with members:
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "one class adversarial nets for fraud detection"
    - "cnn based detection of generic constrast adjustment with jpeg post processing"
    - "abnormal event detection in videos using generative adversarial nets"
    - "learning temporal regularity in video sequences"
    - "the limitations of deep learning in adversarial settings"
    - "evasion attacks against machine learning at test time"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "we need no pixels video manipulation detection using stream descriptors"):
  with members:
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "deepfakes a new threat to face recognition assessment and detection"
    - "a patchmatch based dense field algorithm for video copy move detection and localization"
    - "fast face swap using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "recurrent neural network based semantic variational autoencoder for sequence to sequence learning"):
  with members:
    - "audeep unsupervised learning of representations from audio with deep recurrent neural networks"
    - "attention is all you need"
    - "unsupervised neural machine translation"
    - "machine comprehension by text to text neural question generation"
    - "variational autoencoders for semi supervised text classification"
    - "toward multilingual neural machine translation with universal encoder and decoder"
    - "squad 100 000 questions for machine comprehension of text"
    - "variational neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"):
  with members:
    - "slow feature analysis for human action recognition"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "image to image translation with conditional adversarial networks"
    - "learning temporal regularity in video sequences"

Connected through hyperedge HE-5 (induced by source paper "enabling trust in deep learning models a digital forensics case study"):
  with members:
    - "cloudme forensics a case of big data forensic investigation"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "large scale jpeg image steganalysis using hybrid deep learning framework"
    - "attacking automatic video analysis algorithms a case study of google cloud video intelligence api"
    - "axiomatic attribution for deep networks"
    - "unsupervised machine learning for networking techniques applications and research challenges"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "tiered forensic methodology model for digital field triage by non digital evidence specialists"

Connected through hyperedge HE-6 (induced by source paper "recurrent auto encoder model for large scale industrial sensor signal analysis"):
  with members:
    - "time series compression based on adaptive piecewise recurrent autoencoder"
    - "timenet pre trained deep recurrent neural network for time series classification"
    - "audio word2vec unsupervised learning of audio segment representations using sequence to sequence autoencoder"
    - "adam a method for stochastic optimization"
    - "recurrent neural network regularization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "satellite image forgery detection and localization using gan and one class classifier"):
  with members:
    - "aligned and non aligned double jpeg detection using convolutional neural networks"
    - "mask r cnn"
    - "context encoders feature learning by inpainting"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-8 (induced by source paper "detection of anomalies in large scale accounting data using deep autoencoder networks"):
  with members:
    - "deep structured energy based models for anomaly detection"
    - "an autoencoder approach to learning bilingual word representations"
    - "generalized denoising auto encoders as generative models"
    - "efficient estimation of word representations in vector space"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "scikit learn machine learning in python"

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)