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 "autocorrect deep inductive alignment of noisy geometric annotations"):
  Abstract: We propose AutoCorrect, a method to automatically learn object-annotation alignments from a dataset with annotations affected by geometric noise. The method is based on a consistency loss that enables deep neural networks to be trained, given only noisy annotations as input, to correct the annotations. When some noise-free annotations are available, we show that the consistency loss reduces to a stricter self-supervised loss. We also show that the method can implicitly leverage object symmetries to reduce the ambiguity arising in correcting noisy annotations. When multiple object-annotation pairs are present in an image, we introduce a spatial memory map that allows the network to correct annotations sequentially, one at a time, while accounting for all other annotations in the image and corrections performed so far. Through ablation, we show the benefit of these contributions, demonstrating excellent results on geo-spatial imagery. Specifically, we show results using a new Railway tracks dataset as well as the public INRIA Buildings benchmarks, achieving new state-of-the-art results for the latter.

Includes members:
  Member "smooth loss functions for deep top k classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anchor loss modulating loss scale based on prediction difficulty")
  Member "inductive visual localisation factorised training for superior generalisation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the ladder algorithm finding repetitive structures in medical images by induction")
  Member "deep alignment network a convolutional neural network for robust face alignment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition")
  Member "cycada cycle consistent adversarial domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel framework for selection of gans for an application")
  Member "making neural programming architectures generalize via recursion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "progress extrapolating algorithmic learning to arbitrary sequence lengths")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "learning from noisy large scale datasets with minimal supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mutual mean teaching pseudo label refinery for unsupervised domain adaptation on person re identification")
  Member "spatial memory for context reasoning in object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal rcnn universal object detector via transferable graph r cnn")

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)