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 "unshuffling data for improved generalization"):
  Abstract: The inability to generalize beyond the distribution of a training set is at the core of practical limits of machine learning. We show that the common practice of mixing and shuffling training examples when training deep neural networks is not optimal. On the opposite, partitioning the training data into non-i.i.d. subsets can serve to guide the model to rely on reliable statistical patterns while ignoring spurious correlations in the training data. We demonstrate multiple use cases where these subsets are built using unsupervised clustering, prior knowledge, or other meta-data from existing datasets. The approach is supported by recent results on a causal view of generalization, it is simple to apply, and it demonstrably improves generalization. Applied to the task of visual question answering, we obtain state-of-the-art performance on VQA-CP. We also show improvements over data augmentation using equivalent questions on GQA. Finally, we show a small improvement when training a model simultaneously on VQA v2 and Visual Genome, treating them as two distinct environments rather than one aggregated training set.

Includes members:
  Member "lxmert learning cross modality encoder representations from transformers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "uniter learning universal image text representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "in defense of grid features for visual question answering")
  Member "quantifying and alleviating the language prior problem in visual question answering":
    also connected through other hyperedges:
      (none)
  Member "don t take the easy way out ensemble based methods for avoiding known dataset biases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "adversarial regularization for visual question answering strengths shortcomings and side effects":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "unicoder vl a universal encoder for vision and language by cross modal pre training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "simple but effective techniques to reduce biases":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "rubi reducing unimodal biases in visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unbiased scene graph generation from biased training")

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)