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 "facial action unit detection using attention and relation learning"):
  Abstract: Attention mechanism has recently attracted increasing attentions in the area of facial action unit (AU) detection. By finding the region of interest (ROI) of each AU with the attention mechanism, AU related local features can be captured. Most existing attention based AU detection works use prior knowledge to generate fixed attentions or refine the predefined attentions within a small range, which limits their capacity to model various AUs. In this paper, we propose a novel end-to-end weakly-supervised attention and relation learning framework for AU detection with only AU labels, which has not been explored before. In particular, multi-scale features shared by each AU are learned firstly, and then both channel-wise attentions and spatial attentions are learned to select and extract AU related local features. Moreover, pixel-level relations for AUs are further captured to refine spatial attentions so as to extract more relevant local features. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework (i) outperforms the state-of-the-art methods for AU detection, and (ii) can find the ROI of each AU and capture the relations among AUs adaptively.

Includes members:
  Member "deep structure inference network for facial action unit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "deep adaptive attention for joint facial action unit detection and face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "region based adversarial synthesis of facial action units")
  Member "joint action unit localisation and intensity estimation through heatmap regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatio temporal relation and attention learning for facial action unit detection")
  Member "deep structured learning for facial action unit intensity estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inferring dynamic representations of facial actions from a still image")
  Member "constrained joint cascade regression framework for simultaneous facial action unit recognition and facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "fera 2017 addressing head pose in the third facial expression recognition and analysis challenge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "jaa net joint facial action unit detection and face alignment via adaptive attention")
  Member "picanet learning pixel wise contextual attention for saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "eac net a region based deep enhancing and cropping approach for facial action unit detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "region based adversarial synthesis of facial action units")

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)