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: "transparency by design closing the gap between performance and interpretability in visual reasoning"
  Abstract: Visual question answering requires high-order reasoning about an image, which is a fundamental capability needed by machine systems to follow complex directives. Recently, modular networks have been shown to be an effective framework for performing visual reasoning tasks. While modular networks were initially designed with a degree of model transparency, their performance on complex visual reasoning benchmarks was lacking. Current state-of-the-art approaches do not provide an effective mechanism for understanding the reasoning process. In this paper, we close the performance gap between interpretable models and state-of-the-art visual reasoning methods. We propose a set of visual-reasoning primitives which, when composed, manifest as a model capable of performing complex reasoning tasks in an explicitly-interpretable manner. The fidelity and interpretability of the primitives' outputs enable an unparalleled ability to diagnose the strengths and weaknesses of the resulting model. Critically, we show that these primitives are highly performant, achieving state-of-the-art accuracy of 99.1% on the CLEVR dataset. We also show that our model is able to effectively learn generalized representations when provided a small amount of data containing novel object attributes. Using the CoGenT generalization task, we show more than a 20 percentage point improvement over the current state of the art.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "multi modal deep analysis for multimedia"):
  with members:
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "better and faster knowledge transfer from multiple self supervised learning tasks via graph distillation for video classification"
    - "visual question reasoning on general dependency tree"
    - "learning conditioned graph structures for interpretable visual question answering"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "inferring and executing programs for visual reasoning"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "clevrer collision events for video representation and reasoning"):
  with members:
    - "cater a diagnostic dataset for compositional actions and temporal reasoning"
    - "monet unsupervised scene decomposition and representation"
    - "cophy counterfactual learning of physical dynamics"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "heterogeneous memory enhanced multimodal attention model for video question answering"
    - "from recognition to cognition visual commonsense reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"

Connected through hyperedge HE-4 (induced by source paper "answer them all toward universal visual question answering models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "vqd visual query detection in natural scenes"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "bilinear attention networks"
    - "reciprocal attention fusion for visual question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning visual question answering by bootstrapping hard attention"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "learning by abstraction the neural state machine"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "measuring compositionality in representation learning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "answer them all toward universal visual question answering models"
    - "relation aware graph attention network for visual question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "bilinear attention networks"

Connected through hyperedge HE-6 (induced by source paper "probabilistic neural symbolic models for interpretable visual question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evalai towards better evaluation systems for ai agents"
    - "the neuro symbolic concept learner interpreting scenes words and sentences from natural supervision"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "houdini lifelong learning as program synthesis"
    - "neural guided deductive search for real time program synthesis from examples"
    - "compositional attention networks for machine reasoning"

Connected through hyperedge HE-7 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"
    - "explainable neural computation via stack neural module networks"

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)