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 reasoning over end to end neural architectures for visual question answering"
  Abstract: Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-of-the-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions where additional knowledge is required, and at the same time provides an interpretable interface to the end users. Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based engine to reason over a basket of inputs: visual relations, the semantic parse of the question, and background ontological knowledge from word2vec and ConceptNet. Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate our approach.

Connected through hyperedge HE-1 (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"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"

Connected through hyperedge HE-2 (induced by source paper "scene graph generation with external knowledge and image reconstruction"):
  with members:
    - "semantic correlation promoted shape variant context for segmentation"
    - "graph r cnn for scene graph generation"
    - "scene graph parsing as dependency parsing"
    - "auto encoding scene graphs for image captioning"
    - "unpaired image captioning by language pivoting"
    - "image generation from scene graphs"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "raven a dataset for relational and analogical visual reasoning"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "compositional attention networks for machine reasoning"
    - "visual question reasoning on general dependency tree"
    - "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"
    - "structured attentions for visual question answering"

Connected through hyperedge HE-4 (induced by source paper "integrating knowledge and reasoning in image understanding"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "murel multimodal relational reasoning for visual question answering"
    - "deepproblog neural probabilistic logic programming"
    - "spatial knowledge distillation to aid visual reasoning"
    - "a simple neural network module for relational reasoning"
    - "end to end differentiable proving"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "temporal relational reasoning in videos"

Connected through hyperedge HE-5 (induced by source paper "path ranking with attention to type hierarchies"):
  with members:
    - "tucker tensor factorization for knowledge graph completion"
    - "hierarchical density order embeddings"
    - "deeppath a reinforcement learning method for knowledge graph reasoning"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"
    - "knowledge base completion baselines strike back"
    - "convolutional 2d knowledge graph embeddings"
    - "chains of reasoning over entities relations and text using recurrent neural networks"
    - "listen attend and spell"

Connected through hyperedge HE-6 (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"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "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-7 (induced by source paper "neural symbolic vqa disentangling reasoning from vision and language understanding"):
  with members:
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "compositional attention networks for machine reasoning"
    - "ddrprog a clevr differentiable dynamic reasoning programmer"
    - "visual question reasoning on general dependency tree"
    - "inferring and executing programs for visual reasoning"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "a simple neural network module for relational reasoning"

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"
    - "understand compose and respond answering visual questions by a composition of abstract procedures"
    - "vqa e explaining elaborating and enhancing your answers for visual questions"
    - "object based reasoning in vqa"
    - "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)