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: "object detection can be improved using human derived contextual expectations"
  Abstract: Each object in the world occurs in a specific context: cars are seen on highways but not in forests. Contextual information is generally thought to facilitate computation by constraining locations to search. But can knowing context yield tangible benefits in object detection? For it to do so, scene context needs to be learned independently from target features. However this is impossible in traditional object detection where classifiers are trained on images containing both target features and surrounding coarse scene features. In contrast, we humans have the opportunity to learn context and target features separately, such as when we see highways without cars. Here we show for the first time that human-derived scene expectations can be used to improve object detection performance in machines. To measure these expectations, we asked human subjects to indicate the scale, location and likelihood at which targets may occur on scenes containing no targets. Humans showed highly systematic expectations that we could accurately predict using scene features. We then augmented state-of-the-art object detectors (based on deep neural networks) with these human-derived expectations on novel scenes to produce a significant (1-3%) improvement in detecting cars and people in scenes. This improvement was due to low-confidence detector matches being correctly relabeled as targets when they occurred in likely scenes.

Connected through hyperedge HE-1 (induced by source paper "recognition of advertisement emotions with application to computational advertising"):
  with members:
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "affect recognition in ads with application to computational advertising"
    - "learning transferable architectures for scalable image recognition"
    - "deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders"
    - "evaluating content centric vs user centric ad affect recognition"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "deep feature learning for eeg recordings"
    - "dexpression deep convolutional neural network for expression recognition"

Connected through hyperedge HE-2 (induced by source paper "looking beyond a clever narrative visual context and attention are primary drivers of affect in video advertisements"):
  with members:
    - "multimodal sentiment analysis with word level fusion and reinforcement learning"
    - "yolov3 an incremental improvement"
    - "an eeg based image annotation system"
    - "affect recognition in ads with application to computational advertising"
    - "evaluating content centric vs user centric ad affect recognition"
    - "automatic understanding of image and video advertisements"
    - "pet an eye tracking dataset for animal centric pascal object classes"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (induced by source paper "priming neural networks"):
  with members:
    - "segmentation aware convolutional networks using local attention masks"
    - "modulating early visual processing by language"
    - "film visual reasoning with a general conditioning layer"
    - "stnet selective tuning of convolutional networks for object localization"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-4 (induced by source paper "evaluating content centric vs user centric ad affect recognition"):
  with members:
    - "affect recognition in ads with application to computational advertising"
    - "discovering gender differences in facial emotion recognition via implicit behavioral cues"
    - "caffe convolutional architecture for fast feature embedding"

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)