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: "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
  Abstract: Visual QA is a pivotal challenge for higher-level reasoning, requiring understanding language, vision, and relationships between many objects in a scene. Although datasets like CLEVR are designed to be unsolvable without such complex relational reasoning, some surprisingly simple feed-forward, "holistic" models have recently shown strong performance on this dataset. These models lack any kind of explicit iterative, symbolic reasoning procedure, which are hypothesized to be necessary for counting objects, narrowing down the set of relevant objects based on several attributes, etc. The reason for this strong performance is poorly understood. Hence, our work analyzes such models, and finds that minor architectural elements are crucial to performance. In particular, we find that \textit{early fusion} of language and vision provides large performance improvements. This contrasts with the late fusion approaches popular at the dawn of Visual QA. We propose a simple module we call Multimodal Core, which we hypothesize performs the fundamental operations for multimodal tasks. We believe that understanding why these elements are so important to complex question answering will aid the design of better-performing algorithms for Visual QA while minimizing hand-engineering effort.

Connected through hyperedge HE-1 (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"
    - "bilinear attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "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-2 (induced by source paper "answering questions about data visualizations using efficient bimodal fusion"):
  with members:
    - "towards vqa models that can read"
    - "answer them all toward universal visual question answering models"
    - "bilinear attention networks"
    - "tallyqa answering complex counting questions"
    - "figurenet a deep learning model for question answering on scientific plots"
    - "dvqa understanding data visualizations via question answering"
    - "scatteract automated extraction of data from scatter plots"
    - "inferring and executing programs for visual reasoning"

Connected through hyperedge HE-3 (induced by source paper "pytorchpipe a framework for rapid prototyping of pipelines combining language and vision"):
  with members:
    - "ludwig a type based declarative deep learning toolbox"
    - "leveraging medical visual question answering with supporting facts"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "object based reasoning in vqa"
    - "pythia v0 1 the winning entry to the vqa challenge 2018"
    - "compositional attention networks for machine reasoning"
    - "show ask attend and answer a strong baseline for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "leveraging medical visual question answering with supporting facts"):
  with members:
    - "towards vqa models that can read"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "object based reasoning in vqa"
    - "compositional attention networks for machine reasoning"
    - "on transfer learning using a mac model variant"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "a simple neural network module for relational reasoning"
    - "hadamard product for low rank bilinear pooling"

Connected through hyperedge HE-5 (induced by source paper "what is needed for simple spatial language capabilities in vqa"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "a dataset and architecture for visual reasoning with a working memory"
    - "compositional attention networks for machine reasoning"
    - "inferring and executing programs for visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"
    - "shapeworld a new test methodology for multimodal language understanding"

Connected through hyperedge HE-6 (induced by source paper "generating diverse programs with instruction conditioned reinforced adversarial learning"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "answering visual what if questions from actions to predicted scene descriptions"
    - "learning to understand goal specifications by modelling reward"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "inferring and executing programs for visual reasoning"
    - "c vqa a compositional split of the visual question answering vqa v1 0 dataset"
    - "learning to reason end to end module networks 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)