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 "squinting at vqa models interrogating vqa models with sub questions"):
  Abstract: Existing VQA datasets contain questions with varying levels of complexity. While the majority of questions in these datasets require perception for recognizing existence, properties, and spatial relationships of entities, a significant portion of questions pose challenges that correspond to reasoning tasks -- tasks that can only be answered through a synthesis of perception and knowledge about the world, logic and / or reasoning. This distinction allows us to notice when existing VQA models have consistency issues -- they answer the reasoning question correctly but fail on associated low-level perception questions. For example, models answer the complex reasoning question "Is the banana ripe enough to eat?" correctly, but fail on the associated perception question "Are the bananas mostly green or yellow?" indicating that the model likely answered the reasoning question correctly but for the wrong reason. We quantify the extent to which this phenomenon occurs by creating a new Reasoning split of the VQA dataset and collecting Sub-VQA, a new dataset consisting of 200K new perception questions which serve as sub questions corresponding to the set of perceptual tasks needed to effectively answer the complex reasoning questions in the Reasoning split. Additionally, we propose an approach called Sub-Question Importance-aware Network Tuning (SQuINT), which encourages the model to attend do the same parts of the image when answering the reasoning question and the perception sub questions. We show that SQuINT improves model consistency by 7.8%, also marginally improving its performance on the Reasoning questions in VQA, while also displaying qualitatively better attention maps.

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 "taking a hint leveraging explanations to make vision and language models more grounded":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "gqa a new dataset for real world visual reasoning and compositional question answering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "cycle consistency for robust visual question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "counterfactual samples synthesizing for robust visual question answering")
  Member "multimodal explanations justifying decisions and pointing to the evidence":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robust explanations for visual question answering")
  Member "women also snowboard overcoming bias in captioning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unbiased scene graph generation from biased training")
  Member "pythia v0 1 the winning entry to the vqa challenge 2018":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "in defense of grid features for visual question answering")
  Member "compositional attention networks for machine reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unshuffling data for improved generalization")

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)