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: "pinterest board recommendation for twitter users"
  Abstract: Pinboard on Pinterest is an emerging media to engage online social media users, on which users post online images for specific topics. Regardless of its significance, there is little previous work specifically to facilitate information discovery based on pinboards. This paper proposes a novel pinboard recommendation system for Twitter users. In order to associate contents from the two social media platforms, we propose to use MultiLabel classification to map Twitter user followees to pinboard topics and visual diversification to recommend pinboards given user interested topics. A preliminary experiment on a dataset with 2000 users validated our proposed system.

Connected through hyperedge HE-1 (induced by source paper "learning semantic specific graph representation for multi label image recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"

Connected through hyperedge HE-2 (induced by source paper "woman metal white vs man dress shorts combining social temporal and image signals to understand popularity of pinterest fashion boards"):
  with members:
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "cultural diffusion and trends in facebook photographs"
    - "evolution of fashion brands on twitter and instagram"
    - "densecap fully convolutional localization networks for dense captioning"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"

Connected through hyperedge HE-3 (induced by source paper "improving pairwise ranking for multi label image classification"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "cnn rnn a unified framework for multi label image classification"
    - "multi instance visual semantic embedding"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "deep convolutional ranking for multilabel image annotation"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label text classification revisiting neural networks"

Connected through hyperedge HE-4 (induced by source paper "hashtag centric immersive search on social media"):
  with members:
    - "methods for comparing rankings of search engine results"

Connected through hyperedge HE-5 (induced by source paper "tracking illicit drug dealing and abuse on instagram using multimodal analysis"):
  with members:
    - "finding street gang members on twitter"
    - "webly supervised learning of convolutional networks"
    - "america tweets china a fine grained analysis of the state and individual characteristics regarding attitudes towards china"
    - "social media image analysis for public health"
    - "monitoring potential drug interactions and reactions via network analysis of instagram user timelines"
    - "going deeper with convolutions"

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.SI(Social and Information Networks)