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: "diving deep into sentiment understanding fine tuned cnns for visual sentiment prediction"
  Abstract: Visual media are powerful means of expressing emotions and sentiments. The constant generation of new content in social networks highlights the need of automated visual sentiment analysis tools. While Convolutional Neural Networks (CNNs) have established a new state-of-the-art in several vision problems, their application to the task of sentiment analysis is mostly unexplored and there are few studies regarding how to design CNNs for this purpose. In this work, we study the suitability of fine-tuning a CNN for visual sentiment prediction as well as explore performance boosting techniques within this deep learning setting. Finally, we provide a deep-dive analysis into a benchmark, state-of-the-art network architecture to gain insight about how to design patterns for CNNs on the task of visual sentiment prediction.

Connected through hyperedge HE-1 (induced by source paper "sentiment analysis from images of natural disasters"):
  with members:
    - "automatic detection of passable roads after floods in remote sensed and social media data"
    - "exploiting convnet diversity for flooding identification"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "very deep convolutional networks for large scale image recognition"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-2 (induced by source paper "towards emotion retrieval in egocentric photostream"):
  with members:
    - "sentiment recognition in egocentric photostreams"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "going deeper into first person activity recognition"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "building emotional machines recognizing image emotions through deep neural networks"):
  with members:
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "from pixels to sentiment fine tuning cnns for visual sentiment prediction"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "sentiment recognition in egocentric photostreams"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "building a large scale dataset for image emotion recognition the fine print and the benchmark"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "seeing the big picture deep embedding with contextual evidences"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "learning face representation from scratch"

Connected through hyperedge HE-5 (induced by source paper "from pixels to sentiment fine tuning cnns for visual sentiment prediction"):
  with members:
    - "adaptive computation time for recurrent neural networks"
    - "cultural event recognition with visual convnets and temporal models"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "visual affect around the world a large scale multilingual visual sentiment ontology"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "analyzing the performance of multilayer neural networks for object recognition"
    - "how transferable are features in deep neural networks"

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.MM(Multimedia)