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 "pixel in pixel net towards efficient facial landmark detection in the wild"):
  Abstract: Recently, heatmap regression based models become popular because of their superior performance on locating facial landmarks. However, high-resolution feature maps have to be either generated repeatedly or maintained through the network for such models, which is computationally inefficient for practical applications. Moreover, their generalization capabilities across domains are rarely explored. To address these two problems, we propose Pixel-In-Pixel (PIP) Net for facial landmark detection. The proposed model is equipped with a novel detection head based on heatmap regression. Different from conventional heatmap regression, the new detection head conducts score prediction on low-resolution feature maps. To localize landmarks more precisely, it also conduct offset predictions within each heatmap pixel. By doing this, the inference time is largely reduced without losing accuracy. Besides, we also propose to leverage unlabeled images to improve the generalization capbility of our model through image translation based data distillation. Extensive experiments on four benchmarks show that PIP Net is comparable to state-of-the-arts while running at $27.8$ FPS on a CPU.

Includes members:
  Member "how much position information do convolutional neural networks encode":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "semantic alignment finding semantically consistent ground truth for facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "2d wasserstein loss for robust facial landmark detection")
  Member "teacher supervises students how to learn from partially labeled images for facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3fabrec fast few shot face alignment by reconstruction")
  Member "aggregation via separation boosting facial landmark detector with semi supervised style translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temporal interlacing network")
  Member "high resolution representations for labeling pixels and regions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution")
  Member "look at boundary a boundary aware face alignment algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "kpnet towards minimal face detector")
  Member "wing loss for robust facial landmark localisation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "kpnet towards minimal face detector")
  Member "simple baselines for human pose estimation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

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)