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: "object based reasoning in vqa"
  Abstract: Visual Question Answering (VQA) is a novel problem domain where multi-modal inputs must be processed in order to solve the task given in the form of a natural language. As the solutions inherently require to combine visual and natural language processing with abstract reasoning, the problem is considered as AI-complete. Recent advances indicate that using high-level, abstract facts extracted from the inputs might facilitate reasoning. Following that direction we decided to develop a solution combining state-of-the-art object detection and reasoning modules. The results, achieved on the well-balanced CLEVR dataset, confirm the promises and show significant, few percent improvements of accuracy on the complex "counting" task.

Connected through hyperedge HE-1 (induced by source paper "set structured latent representations"):
  with members:
    - "integrating learning and reasoning with deep logic models"
    - "fspool learning set representations with featurewise sort pooling"
    - "are disentangled representations helpful for abstract visual reasoning"
    - "deep set prediction networks"
    - "multi object representation learning with iterative variational inference"
    - "videograph recognizing minutes long human activities in videos"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "deep sets"

Connected through hyperedge HE-2 (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"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "deep contextualized word representations"

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"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "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 "deep set prediction networks"):
  with members:
    - "fspool learning set representations with featurewise sort pooling"
    - "multi object representation learning with iterative variational inference"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning representations of sets through optimized permutations"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "deep sets"
    - "learning deep generative models of graphs"
    - "concept learning with energy based models"

Connected through hyperedge HE-5 (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"
    - "the visual qa devil in the details the impact of early fusion and batch norm on clevr"
    - "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-6 (induced by source paper "learning visual question answering by bootstrapping hard attention"):
  with members:
    - "hyperbolic attention networks"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "self attention with relative position representations"
    - "relational inductive biases deep learning and graph networks"
    - "compositional attention networks for machine reasoning"
    - "explainable neural computation via stack neural module networks"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "packnet adding multiple tasks to a single network by iterative pruning"

Connected through hyperedge HE-7 (induced by source paper "understand compose and respond answering visual questions by a composition of abstract procedures"):
  with members:
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"
    - "visual question answering datasets algorithms and future challenges"
    - "co attending free form regions and detections with multi modal multiplicative feature embedding for visual question answering"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-8 (induced by source paper "vqa with no questions answers training"):
  with members:
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "tell and answer towards explainable visual question answering using attributes and captions"
    - "explicit reasoning over end to end neural architectures for visual question answering"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "improved fusion of visual and language representations by dense symmetric co attention for visual question answering"
    - "inferring and executing programs for visual reasoning"

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)