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 "adversarial filters of dataset biases"):
  Abstract: Large neural models have demonstrated human-level performance on language and vision benchmarks such as ImageNet and Stanford Natural Language Inference (SNLI). Yet, their performance degrades considerably when tested on adversarial or out-of-distribution samples. This raises the question of whether these models have learned to solve a dataset rather than the underlying task by overfitting on spurious dataset biases. We investigate one recently proposed approach, AFLite, which adversarially filters such dataset biases, as a means to mitigate the prevalent overestimation of machine performance. We provide a theoretical understanding for AFLite, by situating it in the generalized framework for optimum bias reduction. Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks, especially when the benchmarks used for training are over-populated with biased samples. We show that AFLite is broadly applicable to a variety of both real and synthetic datasets for reduction of measurable dataset biases and provide extensive supporting analyses. Finally, filtering results in a large drop in model performance (e.g., from 92% to 63% for SNLI), while human performance still remains high. Our work thus shows that such filtered datasets can pose new research challenges for robust generalization by serving as upgraded benchmarks.

Includes members:
  Member "dataset distillation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "progressive compressed records taking a byte out of deep learning data")
  Member "hellaswag can a machine really finish your sentence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "right for the wrong reasons diagnosing syntactic heuristics in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "inoculation by fine tuning a method for analyzing challenge datasets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "what does my qa model know devising controlled probes using expert knowledge")
  Member "repair removing representation bias by dataset resampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unbiased scene graph generation from biased training")
  Member "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "paraphrase generation with latent bag of words")
  Member "natural adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "randaugment practical automated data augmentation with a reduced search space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

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.LG(Machine Learning)