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 "domain translation with conditional gans from depth to rgb face to face"):
  Abstract: Can faces acquired by low-cost depth sensors be useful to catch some characteristic details of the face? Typically the answer is no. However, new deep architectures can generate RGB images from data acquired in a different modality, such as depth data. In this paper, we propose a new \textit{Deterministic Conditional GAN}, trained on annotated RGB-D face datasets, effective for a face-to-face translation from depth to RGB. Although the network cannot reconstruct the exact somatic features for unknown individual faces, it is capable to reconstruct plausible faces; their appearance is accurate enough to be used in many pattern recognition tasks. In fact, we test the network capability to hallucinate with some \textit{Perceptual Probes}, as for instance face aspect classification or landmark detection. Depth face can be used in spite of the correspondent RGB images, that often are not available due to difficult luminance conditions. Experimental results are very promising and are as far as better than previously proposed approaches: this domain translation can constitute a new way to exploit depth data in new future applications.

Includes members:
  Member "transforming sensor data to the image domain for deep learning an application to footstep detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis")
  Member "from depth data to head pose estimation a siamese approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a differential approach for gaze estimation")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "image restoration using autoencoding priors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning generative models using denoising density estimators")
  Member "deep head pose estimation from depth data for in car automotive applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combining deep and depth deep learning and face depth maps for driver attention monitoring")
  Member "face from depth for head pose estimation on depth images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combining deep and depth deep learning and face depth maps for driver attention monitoring")
  Member "generative adversarial models for people attribute recognition in surveillance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pedestrian attribute recognition a survey")
  Member "poseidon face from depth for driver pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention aware fusion rgb d face recognition")

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)