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 "self orthogonality module a network architecture plug in for learning orthogonal filters"):
  Abstract: In this paper, we investigate the empirical impact of orthogonality regularization (OR) in deep learning, either solo or collaboratively. Recent works on OR showed some promising results on the accuracy. In our ablation study, however, we do not observe such significant improvement from existing OR techniques compared with the conventional training based on weight decay, dropout, and batch normalization. To identify the real gain from OR, inspired by the locality sensitive hashing (LSH) in angle estimation, we propose to introduce an implicit self-regularization into OR to push the mean and variance of filter angles in a network towards 90 and 0 simultaneously to achieve (near) orthogonality among the filters, without using any other explicit regularization. Our regularization can be implemented as an architectural plug-in and integrated with an arbitrary network. We reveal that OR helps stabilize the training process and leads to faster convergence and better generalization.

Includes members:
  Member "orthogonal deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "unsupervised deep feature transfer for low resolution image classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mdfn multi scale deep feature learning network for object detection")
  Member "adversarially approximated autoencoder for image generation and manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zoomcount a zooming mechanism for crowd counting in static images")
  Member "mdfn multi scale deep feature learning network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zoomcount a zooming mechanism for crowd counting in static images")
  Member "towards learning affine invariant representations via data efficient cnns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "zoomcount a zooming mechanism for crowd counting in static images")
  Member "boosting occluded image classification via subspace decomposition based estimation of deep features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zoomcount a zooming mechanism for crowd counting in static images")
  Member "interpretable convolutional neural networks via feedforward design":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "predicting trends in the quality of state of the art neural networks without access to training or testing data")

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)