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 "seeing beyond appearance mapping real images into geometrical domains for unsupervised cad based recognition"):
  Abstract: While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their training. It thus became common to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance. As this mapping is easier to learn than the opposite one (ie to learn to generate realistic features to augment the source samples), we demonstrate how our whole solution can be trained purely on augmented synthetic data, and still perform better than methods trained with domain-relevant information (eg real images or realistic textures for the 3D models). Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve a more refined mapping for unseen color images.

Includes members:
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deceptionnet network driven domain randomization")
  Member "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "unsupervised reverse domain adaptation for synthetic medical images via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "predicting deeper into the future of semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "multi task learning using uncertainty to weigh losses for scene geometry and semantics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatial sampling network for fast scene understanding")
  Member "exploiting depth from single monocular images for object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "incremental scene synthesis")
  Member "t less an rgb d dataset for 6d pose estimation of texture less objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepim deep iterative matching for 6d pose estimation")

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)