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 "jointly aligning millions of images with deep penalised reconstruction congealing"):
  Abstract: Extrapolating fine-grained pixel-level correspondences in a fully unsupervised manner from a large set of misaligned images can benefit several computer vision and graphics problems, e.g. co-segmentation, super-resolution, image edit propagation, structure-from-motion, and 3D reconstruction. Several joint image alignment and congealing techniques have been proposed to tackle this problem, but robustness to initialisation, ability to scale to large datasets, and alignment accuracy seem to hamper their wide applicability. To overcome these limitations, we propose an unsupervised joint alignment method leveraging a densely fused spatial transformer network to estimate the warping parameters for each image and a low-capacity auto-encoder whose reconstruction error is used as an auxiliary measure of joint alignment. Experimental results on digits from multiple versions of MNIST (i.e., original, perturbed, affNIST and infiMNIST) and faces from LFW, show that our approach is capable of aligning millions of images with high accuracy and robustness to different levels and types of perturbation. Moreover, qualitative and quantitative results suggest that the proposed method outperforms state-of-the-art approaches both in terms of alignment quality and robustness to initialisation.

Includes members:
  Member "destnet densely fused spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "explicit disentanglement of appearance and perspective in generative models")
  Member "convolutional neural network architecture for geometric matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "face detection using deep learning an improved faster rcnn approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding interesting frames in deep video analytics a top k approach")
  Member "warpnet weakly supervised matching for single view reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos")
  Member "large scale image retrieval with attentive deep local features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "inverse compositional spatial transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "joint face detection and alignment using multitask cascaded convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "supervised transformer network for efficient face detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "kpnet towards minimal face detector")

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)