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 "signed input regularization"):
  Abstract: Over-parameterized deep models usually over-fit to a given training distribution, which makes them sensitive to small changes and out-of-distribution samples at inference time, leading to low generalization performance. To this end, several model-based and randomized data-dependent regularization methods are applied, such as data augmentation, which prevents a model from memorizing the training distribution. Instead of the random transformation of the input images, we propose SIGN, a new regularization method, which modifies the input variables using a linear transformation by estimating each variable's contribution to the final prediction. Our proposed technique maps the input data to a new manifold where the less important variables are de-emphasized. To test the effectiveness of the proposed idea and compare it with other competing methods, we design several test scenarios, such as classification performance, uncertainty, out-of-distribution, and robustness analyses. We compare the methods using three different datasets and four models. We find that SIGN encourages more compact class representations, which results in the model's robustness to random corruptions and out-of-distribution samples while also simultaneously achieving superior performance on normal data compared to other competing methods. Our experiments also demonstrate the successful transferability of the SIGN samples from one model to another.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "deep learning vs traditional computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from learning machines optimisation rules and social norms")
  Member "adversarial training generalizes data dependent spectral norm regularization":
    also connected through other hyperedges:
      (none)
  Member "improved inference via deep input transfer":
    also connected through other hyperedges:
      (none)
  Member "metamixup learning adaptive interpolation policy of mixup with meta learning":
    also connected through other hyperedges:
      (none)
  Member "a meta transfer objective for learning to disentangle causal mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "data interpolating prediction alternative interpretation of mixup":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mixup inference better exploiting mixup to defend adversarial attacks")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")

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)