human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "zooming into face forensics a pixel level analysis"):
  Abstract: The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society. It is urgent to have face forensics techniques to distinguish those tampered images. A large scale dataset "FaceForensics++" has provided enormous training data generated from prominent face manipulation methods to facilitate anti-fake research. However, previous works focus more on casting it as a classification problem by only considering a global prediction. Through investigation to the problem, we find that training a classification network often fails to capture high quality features, which might lead to sub-optimal solutions. In this paper, we zoom in on the problem by conducting a pixel-level analysis, i.e. formulating it as a pixel-level segmentation task. By evaluating multiple architectures on both segmentation and classification tasks, We show the superiority of viewing the problem from a segmentation perspective. Different ablation studies are also performed to investigate what makes an effective and efficient anti-fake model. Strong baselines are also established, which, we hope, could shed some light on the field of face forensics.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "deferred neural rendering image synthesis using neural textures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "faceforensics learning to detect manipulated facial images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "detecting photoshopped faces by scripting photoshop":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "fighting fake news image splice detection via learned self consistency":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "mesonet a compact facial video forgery detection network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images")
  Member "face aging with conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "algorithmic fairness")

Question: Please tell me which class the source paper 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)