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: "a patchmatch based dense field algorithm for video copy move detection and localization"
  Abstract: We propose a new algorithm for the reliable detection and localization of video copy-move forgeries. Discovering well crafted video copy-moves may be very difficult, especially when some uniform background is copied to occlude foreground objects. To reliably detect both additive and occlusive copy-moves we use a dense-field approach, with invariant features that guarantee robustness to several post-processing operations. To limit complexity, a suitable video-oriented version of PatchMatch is used, with a multiresolution search strategy, and a focus on volumes of interest. Performance assessment relies on a new dataset, designed ad hoc, with realistic copy-moves and a wide variety of challenging situations. Experimental results show the proposed method to detect and localize video copy-moves with good accuracy even in adverse conditions.

Connected through hyperedge HE-1 (induced by source paper "learning transformation aware embeddings for image forensics"):
  with members:
    - "learning rich features for image manipulation detection"
    - "classification is a strong baseline for deep metric learning"
    - "fighting fake news image splice detection via learned self consistency"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "beyond pixels image provenance analysis leveraging metadata"
    - "image processing operations identification via convolutional neural network"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "no fuss distance metric learning using proxies"

Connected through hyperedge HE-2 (induced by source paper "faceforensics learning to detect manipulated facial images"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "learning rich features for image manipulation detection"
    - "headon real time reenactment of human portrait videos"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "two stream neural networks for tampered face detection"

Connected through hyperedge HE-3 (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"
    - "autoencoder with recurrent neural networks for video forgery detection"
    - "fast face swap using convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "attributing fake images to gans learning and analyzing gan fingerprints"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "learning rich features for image manipulation detection"
    - "non stationary texture synthesis by adversarial expansion"
    - "do gans leave artificial fingerprints"
    - "fighting fake news image splice detection via learned self consistency"
    - "mesonet a compact facial video forgery detection network"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"

Connected through hyperedge HE-5 (induced by source paper "attributing fake images to gans analyzing fingerprints in generated images"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "learning rich features for image manipulation detection"
    - "non stationary texture synthesis by adversarial expansion"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "spectral normalization for generative adversarial networks"
    - "noiseprint a cnn based camera model fingerprint"

Connected through hyperedge HE-6 (induced by source paper "a coarse to fine deep convolutional neural network framework for frame duplication detection and localization in video forgery"):
  with members:
    - "domain adaptive faster r cnn for object detection in the wild"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "convolutional image captioning"
    - "convnets and imagenet beyond accuracy understanding mistakes and uncovering biases"
    - "a survey on video forgery detection"
    - "deep residual learning for image recognition"

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)