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: "context dependent diffusion network for visual relationship detection"
  Abstract: Visual relationship detection can bridge the gap between computer vision and natural language for scene understanding of images. Different from pure object recognition tasks, the relation triplets of subject-predicate-object lie on an extreme diversity space, such asperson-behind-person andcar-behind-building, while suffering from the problem of combinatorial explosion. In this paper, we propose a context-dependent diffusion network (CDDN) framework to deal with visual relationship detection. To capture the interactions of different object instances, two types of graphs, word semantic graph and visual scene graph, are constructed to encode global context interdependency. The semantic graph is built through language priors to model semantic correlations across objects, whilst the visual scene graph defines the connections of scene objects so as to utilize the surrounding scene information. For the graph-structured data, we design a diffusion network to adaptively aggregate information from contexts, which can effectively learn latent representations of visual relationships and well cater to visual relationship detection in view of its isomorphic invariance to graphs. Experiments on two widely-used datasets demonstrate that our proposed method is more effective and achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "visual relationship detection with relative location mining"):
  with members:
    - "linknet relational embedding for scene graph"
    - "shuffle then assemble learning object agnostic visual relationship features"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "vip cnn visual phrase guided convolutional neural network"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-2 (induced by source paper "video relationship reasoning using gated spatio temporal energy graph"):
  with members:
    - "discovering order in unordered datasets generative markov networks"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "videos as space time region graphs"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"
    - "detecting visual relationships with deep relational networks"

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"
    - "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"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "visual relationship detection based on guided proposals and semantic knowledge distillation"
    - "learn to pay attention"

Connected through hyperedge HE-4 (induced by source paper "interpreting context of images using scene graphs"):
  with members:
    - "graph r cnn for scene graph generation"
    - "factorizable net an efficient subgraph based framework for scene graph generation"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"

Connected through hyperedge HE-5 (induced by source paper "gaussian induced convolution for graphs"):
  with members:
    - "tensor graph convolutional neural network"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "shift aggregate extract networks"
    - "spectral filter tracking"
    - "learning graph representations with embedding propagation"

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)