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: "multi task learning for detecting and segmenting manipulated facial images and videos"
  Abstract: Detecting manipulated images and videos is an important topic in digital media forensics. Most detection methods use binary classification to determine the probability of a query being manipulated. Another important topic is locating manipulated regions (i.e., performing segmentation), which are mostly created by three commonly used attacks: removal, copy-move, and splicing. We have designed a convolutional neural network that uses the multi-task learning approach to simultaneously detect manipulated images and videos and locate the manipulated regions for each query. Information gained by performing one task is shared with the other task and thereby enhance the performance of both tasks. A semi-supervised learning approach is used to improve the network's generability. The network includes an encoder and a Y-shaped decoder. Activation of the encoded features is used for the binary classification. The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input, which helps improve overall performance. Experiments using the FaceForensics and FaceForensics++ databases demonstrated the network's effectiveness against facial reenactment attacks and face swapping attacks as well as its ability to deal with the mismatch condition for previously seen attacks. Moreover, fine-tuning using just a small amount of data enables the network to deal with unseen attacks.

Connected through hyperedge HE-1 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "detecting gan generated fake images using co occurrence matrices"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "analyzing and improving the image quality of stylegan"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "celeb df a new dataset for deepfake forensics"

Connected through hyperedge HE-2 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "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-3 (induced by source paper "deeperforensics 1 0 a large scale dataset for real world face forgery detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "look at boundary a boundary aware face alignment algorithm"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"

Connected through hyperedge HE-4 (induced by source paper "fakelocator robust localization of gan based face manipulations via semantic segmentation networks with bells and whistles"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "analyzing and improving the image quality of stylegan"
    - "face x ray for more general face forgery detection"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "detecting and simulating artifacts in gan fake images"
    - "stgan a unified selective transfer network for arbitrary image attribute editing"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "on the detection of digital face manipulation"

Connected through hyperedge HE-5 (induced by source paper "image embedded segmentation combining supervised and unsupervised objectives through generative adversarial networks"):
  with members:
    - "mild net minimal information loss dilated network for gland instance segmentation in colon histology images"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "gland instance segmentation using deep multichannel neural networks"
    - "semantic segmentation using adversarial networks"
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "emotions don t lie a deepfake detection method using audio visual affective cues"):
  with members:
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "celeb df a new dataset for deepfake forensics"
    - "mesonet a compact facial video forgery detection network"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "capsule forensics using capsule networks to detect forged images and videos"
    - "two stream neural networks for tampered face detection"

Connected through hyperedge HE-7 (induced by source paper "on the detection of digital face manipulation"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "deep tree learning for zero shot face anti spoofing"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"

Connected through hyperedge HE-8 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "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"
    - "pedestrian trajectory prediction with structured memory hierarchies"
    - "social gan socially acceptable trajectories with generative 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)