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: "improving visual relationship detection using semantic modeling of scene descriptions"
  Abstract: Structured scene descriptions of images are useful for the automatic processing and querying of large image databases. We show how the combination of a semantic and a visual statistical model can improve on the task of mapping images to their associated scene description. In this paper we consider scene descriptions which are represented as a set of triples (subject, predicate, object), where each triple consists of a pair of visual objects, which appear in the image, and the relationship between them (e.g. man-riding-elephant, man-wearing-hat). We combine a standard visual model for object detection, based on convolutional neural networks, with a latent variable model for link prediction. We apply multiple state-of-the-art link prediction methods and compare their capability for visual relationship detection. One of the main advantages of link prediction methods is that they can also generalize to triples, which have never been observed in the training data. Our experimental results on the recently published Stanford Visual Relationship dataset, a challenging real world dataset, show that the integration of a semantic model using link prediction methods can significantly improve the results for visual relationship detection. Our combined approach achieves superior performance compared to the state-of-the-art method from the Stanford computer vision group.

Connected through hyperedge HE-1 (induced by source paper "debate dynamics for human comprehensible fact checking on knowledge graphs"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "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"
    - "convolutional 2d knowledge graph embeddings"
    - "why should i trust you explaining the predictions of any classifier"
    - "complex embeddings for simple link prediction"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-2 (induced by source paper "reasoning on knowledge graphs with debate dynamics"):
  with members:
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "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"
    - "convolutional 2d knowledge graph embeddings"
    - "complex embeddings for simple link prediction"

Connected through hyperedge HE-3 (induced by source paper "deeply supervised multimodal attentional translation embeddings for visual relationship detection"):
  with members:
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "context dependent diffusion network for visual relationship detection"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "detecting visual relationships using box attention"
    - "large scale visual relationship understanding"
    - "an interpretable model for scene graph generation"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "learn to pay attention"

Connected through hyperedge HE-4 (induced by source paper "a boxology of design patterns for hybrid learning and reasoning systems"):
  with members:
    - "model free model based and general intelligence"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "reasoning over rdf knowledge bases using deep learning"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "knowledge based transfer learning explanation"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-5 (induced by source paper "improving visual relation detection using depth maps"):
  with members:
    - "graph r cnn for scene graph generation"
    - "neural motifs scene graph parsing with global context"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "visual translation embedding network for visual relation detection"
    - "visual genome connecting language and vision using crowdsourced dense image annotations"
    - "visual relationship detection with language priors"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "compensating supervision incompleteness with prior knowledge in semantic image interpretation"):
  with members:
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "scene graph generation by iterative message passing"
    - "visual relationship detection with internal and external linguistic knowledge distillation"
    - "logic tensor networks for semantic image interpretation"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"

Connected through hyperedge HE-7 (induced by source paper "knowledge representation learning a quantitative review"):
  with members:
    - "on the equivalence of holographic and complex embeddings for link prediction"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "flexible end to end dialogue system for knowledge grounded conversation"
    - "a neural knowledge language model"
    - "analogical inference for multi relational embeddings"
    - "visual translation embedding network for visual relation detection"
    - "probabilistic reasoning via deep learning neural association models"
    - "ssp semantic space projection for knowledge graph embedding with text descriptions"

Connected through hyperedge HE-8 (induced by source paper "improving information extraction from images with learned semantic models"):
  with members:
    - "the tensor memory hypothesis"
    - "visual translation embedding network for visual relation detection"
    - "visual relationship detection with language priors"
    - "learning with memory embeddings"
    - "very deep convolutional networks for large scale image recognition"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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.AI(Artificial Intelligence)