human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "explicit bias discovery in visual question answering models"
  Abstract: Researchers have observed that Visual Question Answering (VQA) models tend to answer questions by learning statistical biases in the data. For example, their answer to the question "What is the color of the grass?" is usually "Green", whereas a question like "What is the title of the book?" cannot be answered by inferring statistical biases. It is of interest to the community to explicitly discover such biases, both for understanding the behavior of such models, and towards debugging them. Our work address this problem. In a database, we store the words of the question, answer and visual words corresponding to regions of interest in attention maps. By running simple rule mining algorithms on this database, we discover human-interpretable rules which give us unique insight into the behavior of such models. Our results also show examples of unusual behaviors learned by models in attempting VQA tasks.

Connected through hyperedge HE-1 (induced by source paper "unbiased scene graph generation from biased training"):
  with members:
    - "visual commonsense r cnn"
    - "deconfounded image captioning a causal retrospect"
    - "knowledge embedded routing network for scene graph generation"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "learning by abstraction the neural state machine"
    - "repair removing representation bias by dataset resampling"
    - "two causal principles for improving visual dialog"

Connected through hyperedge HE-2 (induced by source paper "towards ground truth evaluation of visual explanations"):
  with members:
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "methods for interpreting and understanding deep neural networks"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "a simple neural network module for relational reasoning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-3 (induced by source paper "machines learn appearance bias in face recognition"):
  with members:
    - "saving face investigating the ethical concerns of facial recognition auditing"
    - "predictive inequity in object detection"
    - "women also snowboard overcoming bias in captioning models"
    - "first impressions a survey on vision based apparent personality trait analysis"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-4 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "multi task deep neural networks for natural language understanding"
    - "modeling naive psychology of characters in simple commonsense stories"
    - "from recognition to cognition visual commonsense reasoning"
    - "annotation artifacts in natural language inference data"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "gender bias in coreference resolution"
    - "a simple method for commonsense reasoning"

Connected through hyperedge HE-5 (induced by source paper "rubi reducing unimodal biases in visual question answering"):
  with members:
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "right for the right reason training agnostic networks"
    - "bilinear attention networks"
    - "from recognition to cognition visual commonsense reasoning"
    - "women also snowboard overcoming bias in captioning models"
    - "vizwiz grand challenge answering visual questions from blind people"

Connected through hyperedge HE-6 (induced by source paper "exposing and correcting the gender bias in image captioning datasets and models"):
  with members:
    - "right for the right reason training agnostic networks"
    - "turning a blind eye explicit removal of biases and variation from deep neural network embeddings"
    - "women also snowboard overcoming bias in captioning models"
    - "mitigating unwanted biases with adversarial learning"
    - "adversarial removal of demographic attributes from text data"
    - "swag a large scale adversarial dataset for grounded commonsense inference"
    - "learning gender neutral word embeddings"
    - "gender bias in coreference resolution evaluation and debiasing methods"

Connected through hyperedge HE-7 (induced by source paper "spatialsense an adversarially crowdsourced benchmark for spatial relation recognition"):
  with members:
    - "linknet relational embedding for scene graph"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "being negative but constructively lessons learnt from creating better visual question answering datasets"
    - "an analysis of visual question answering algorithms"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-8 (induced by source paper "towards causal vqa revealing and reducing spurious correlations by invariant and covariant semantic editing"):
  with members:
    - "sunny and dark outside improving answer consistency in vqa through entailed question generation"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "cycle consistency for robust visual question answering"
    - "adversarial scene editing automatic object removal from weak supervision"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "visual question answering datasets algorithms and future challenges"
    - "show ask attend and answer a strong baseline for visual question answering"

Question: Please tell me which class the center node 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)