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 "learning to train with synthetic humans"):
  Abstract: Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans and a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. Using the augmented dataset, without considering synthetic humans in the loss, leads to the best results. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that the student-teacher framework outperforms normal training on the purely synthetic dataset.

Includes members:
  Member "lcr net multi person 2d and 3d pose detection in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from kinematics to dynamics estimating center of pressure and base of support from video frames of human motion")
  Member "learning human optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning multi human optical flow")
  Member "improving spatiotemporal self supervision by deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised spatio temporal representation learning using variable playback speed prediction")
  Member "image based synthesis for deep 3d human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting temporal context for 3d human pose estimation in the wild")
  Member "learning to refine human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object contextual representations for semantic segmentation")
  Member "a closed form solution to photorealistic image stylization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning texture invariant representation for domain adaptation of semantic segmentation")
  Member "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition")
  Member "how robust is 3d human pose estimation to occlusion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "metric scale truncation robust heatmaps for 3d human 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)