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: "exploring person context and local scene context for object detection"
  Abstract: In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).

Connected through hyperedge HE-1 (induced by source paper "actor centric relation network"):
  with members:
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "slac a sparsely labeled dataset for action classification and localization"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "a simple neural network module for relational reasoning"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "weakly supervised learning of visual relations"

Connected through hyperedge HE-2 (induced by source paper "structure inference net object detection using scene level context and instance level relationships"):
  with members:
    - "focal loss for dense object detection"
    - "scene graph generation by iterative message passing"
    - "dsod learning deeply supervised object detectors from scratch"
    - "spatial memory for context reasoning in object detection"
    - "ssd single shot multibox detector"
    - "gated graph sequence neural networks"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint object detection"

Connected through hyperedge HE-3 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "relation networks for object detection"):
  with members:
    - "detecting and recognizing human object interactions"
    - "learning transferable architectures for scalable image recognition"
    - "visual interaction networks"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "a simple neural network module for relational reasoning"
    - "deformable convolutional networks"
    - "vain attentional multi agent predictive modeling"

Connected through hyperedge HE-5 (induced by source paper "object level context modeling for scene classification with context cnn"):
  with members:
    - "locally supervised deep hybrid model for scene recognition"
    - "semantic object parsing with graph lstm"
    - "cnn rnn a unified framework for multi label image classification"
    - "multimodal compact bilinear pooling for visual question answering and visual grounding"
    - "fusing multi stream deep networks for video classification"
    - "exploring models and data for image question answering"
    - "semantic object parsing with local global long short term memory"
    - "vqa visual question answering"

Connected through hyperedge HE-6 (induced by source paper "teaching compositionality to cnns"):
  with members:
    - "object centric representation learning from unlabeled videos"
    - "attend infer repeat fast scene understanding with generative models"
    - "the curious robot learning visual representations via physical interactions"
    - "towards deep compositional networks"
    - "deep learning in neural networks"
    - "learning to see by moving"
    - "learning and or models to represent context and occlusion for car detection and viewpoint estimation"
    - "slow and steady feature analysis higher order temporal coherence in video"

Connected through hyperedge HE-7 (induced by source paper "beyond skip connections top down modulation for object detection"):
  with members:
    - "optical flow estimation using a spatial pyramid network"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "learning to refine object segments"
    - "learning to segment object candidates"
    - "holistically nested edge detection"
    - "iterative instance segmentation"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-8 (induced by source paper "stuffnet using stuff to improve object detection"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "cross stitch networks for multi task learning"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "instance aware semantic segmentation via multi task network cascades"
    - "you only look once unified real time object detection"
    - "conditional random fields as recurrent neural networks"
    - "parsenet looking wider to see better"

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)