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: "hybrid lstm and encoder decoder architecture for detection of image forgeries"
  Abstract: With advanced image journaling tools, one can easily alter the semantic meaning of an image by exploiting certain manipulation techniques such as copy clone, object splicing, and removal, which mislead the viewers. In contrast, the identification of these manipulations becomes a very challenging task as manipulated regions are not visually apparent. This paper proposes a high-confidence manipulation localization architecture that utilizes resampling features, long short-term memory (LSTM) cells, and an encoder-decoder network to segment out manipulated regions from non-manipulated ones. Resampling features are used to capture artifacts, such as JPEG quality loss, upsampling, downsampling, rotation, and shearing. The proposed network exploits larger receptive fields (spatial maps) and frequency-domain correlation to analyze the discriminative characteristics between the manipulated and non-manipulated regions by incorporating the encoder and LSTM network. Finally, the decoder network learns the mapping from low-resolution feature maps to pixel-wise predictions for image tamper localization. With the predicted mask provided by the final layer (softmax) of the proposed architecture, end-to-end training is performed to learn the network parameters through back-propagation using the ground-truth masks. Furthermore, a large image splicing dataset is introduced to guide the training process. The proposed method is capable of localizing image manipulations at the pixel level with high precision, which is demonstrated through rigorous experimentation on three diverse datasets.

Connected through hyperedge HE-1 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "use of a capsule network to detect fake images and videos"
    - "learning rich features for image manipulation detection"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-2 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-3 (induced by source paper "face x ray for more general face forgery detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "on the generalization of gan image forensics"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "celeb df a new dataset for deepfake forensics"
    - "incremental learning for the detection and classification of gan generated images"
    - "swapped face detection using deep learning and subjective assessment"

Connected through hyperedge HE-4 (induced by source paper "multi task learning for detecting and segmenting manipulated facial images and videos"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "learning rich features for image manipulation detection"
    - "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"
    - "deepfakes a new threat to face recognition assessment and detection"
    - "recasting residual based local descriptors as convolutional neural networks an application to image forgery detection"

Connected through hyperedge HE-5 (induced by source paper "a full image full resolution end to end trainable cnn framework for image forgery detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "noiseprint a cnn based camera model fingerprint"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "camera based image forgery localization using convolutional neural networks"
    - "boundary based image forgery detection by fast shallow cnn"

Connected through hyperedge HE-6 (induced by source paper "celeb df a new dataset for deepfake forensics"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "use of a capsule network to detect fake images and videos"
    - "learning rich features for image manipulation detection"
    - "exposing deep fakes using inconsistent head poses"
    - "faceforensics a large scale video dataset for forgery detection in human faces"

Connected through hyperedge HE-7 (induced by source paper "constrained r cnn a general image manipulation detection model"):
  with members:
    - "learning rich features for image manipulation detection"
    - "image splicing localization using a multi task fully convolutional network mfcn"
    - "cbam convolutional block attention module"
    - "detection and localization of image forgeries using resampling features and deep learning"
    - "mask r cnn"
    - "faster r cnn towards real time object detection with region proposal 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)