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: "detecting visual relationships using box attention"
  Abstract: In this paper we propose a new model for detecting visual relationships. Our main technical novelty is a Box Attention mechanism that allows modelling pairwise interactions between objects in visual scenes using standard object detection pipelines. The resulting model is conceptually clean, expressive and relies on well-justified training and prediction procedures. Moreover, unlike previously proposed approaches, our model does not introduce any additional complex components or hyperparameters on top of those already required by the underlying detection model. We conduct an experimental evaluation on two challenging datasets, V-COCO and Visual Relationships, demonstrating strong quantitative and qualitative results.

Connected through hyperedge HE-1 (induced by source paper "vsgnet spatial attention network for detecting human object interactions using graph convolutions"):
  with members:
    - "learning actor relation graphs for group activity recognition"
    - "relation aware graph attention network for visual question answering"
    - "actor conditioned attention maps for video action detection"
    - "ican instance centric attention network for human object interaction detection"
    - "unified perceptual parsing for scene understanding"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "detecting and recognizing human object interactions"

Connected through hyperedge HE-2 (induced by source paper "classifying all interacting pairs in a single shot"):
  with members:
    - "detecting human object interactions via functional generalization"
    - "detecting unseen visual relations using analogies"
    - "ican instance centric attention network for human object interaction detection"
    - "learning human object interactions by graph parsing neural networks"
    - "transferable interactiveness knowledge for human object interaction detection"
    - "object level visual reasoning in videos"
    - "detecting and recognizing human object interactions"
    - "focal loss for dense object detection"

Connected through hyperedge HE-3 (induced by source paper "detecting human object interactions via functional generalization"):
  with members:
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "interact as you intend intention driven human object interaction detection"
    - "detecting unseen visual relations using analogies"
    - "women also snowboard overcoming bias in captioning models"
    - "ican instance centric attention network for human object interaction detection"
    - "zero shot object detection by hybrid region embedding"
    - "no frills human object interaction detection factorization layout encodings and training techniques"
    - "pairwise body part attention for recognizing human object interactions"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "deep contextual attention for human object interaction detection"):
  with members:
    - "interact as you intend intention driven human object interaction detection"
    - "ican instance centric attention network for human object interaction detection"
    - "learning human object interactions by graph parsing neural networks"
    - "structure inference net object detection using scene level context and instance level relationships"
    - "generative image inpainting with contextual attention"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"
    - "learning to detect human object interactions"

Connected through hyperedge HE-6 (induced by source paper "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "ican instance centric attention network for human object interaction detection"
    - "vip cnn visual phrase guided convolutional neural network"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "detecting and recognizing human object interactions"
    - "focal loss for dense object detection"
    - "neural motifs scene graph parsing with global context"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-7 (induced by source paper "ican instance centric attention network for human object interaction detection"):
  with members:
    - "learn to pay attention"
    - "attention is all you need"
    - "neural motifs scene graph parsing with global context"
    - "weakly supervised learning of visual relations"
    - "detecting visual relationships with deep relational networks"
    - "scene graph generation by iterative message passing"
    - "scene graph generation from objects phrases and region captions"
    - "mask r cnn"

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)