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: "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"
  Abstract: Recommending products to consumers means not only understanding their tastes, but also understanding their level of experience. For example, it would be a mistake to recommend the iconic film Seven Samurai simply because a user enjoys other action movies; rather, we might conclude that they will eventually enjoy it -- once they are ready. The same is true for beers, wines, gourmet foods -- or any products where users have acquired tastes: the `best' products may not be the most `accessible'. Thus our goal in this paper is to recommend products that a user will enjoy now, while acknowledging that their tastes may have changed over time, and may change again in the future. We model how tastes change due to the very act of consuming more products -- in other words, as users become more experienced. We develop a latent factor recommendation system that explicitly accounts for each user's level of experience. We find that such a model not only leads to better recommendations, but also allows us to study the role of user experience and expertise on a novel dataset of fifteen million beer, wine, food, and movie reviews.

Connected through hyperedge HE-1 (induced by source paper "temporal probability calibration"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "when does label smoothing help"
    - "huggingface s transformers state of the art natural language processing"
    - "measuring calibration in deep learning"
    - "pytorch an imperative style high performance deep learning library"
    - "probability calibration trees"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "style example guided text generation using generative adversarial transformers"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "real or fake learning to discriminate machine from human generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "pretraining based natural language generation for text summarization"
    - "multi task deep neural networks for natural language understanding"
    - "domain adaptive text style transfer"
    - "defending against neural fake news"

Connected through hyperedge HE-3 (induced by source paper "evaluating defensive distillation for defending text processing neural networks against adversarial examples"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep text classification can be fooled"
    - "sok security and privacy in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards crafting text adversarial samples"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "extending defensive distillation"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-4 (induced by source paper "i know you ll be back interpretable new user clustering and churn prediction on a mobile social application"):
  with members:
    - "parallel long short term memory for multi stream classification"
    - "cone community oriented network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "improved semantic representations from tree structured long short term memory networks"
    - "line large scale information network embedding"

Connected through hyperedge HE-5 (induced by source paper "sentiment analysis based on rhetorical structure theory learning deep neural networks from discourse trees"):
  with members:
    - "deep learning in business analytics and operations research models applications and managerial implications"
    - "deep learning for affective computing text based emotion recognition in decision support"
    - "learning structured text representations"
    - "decision support from financial disclosures with deep neural networks and transfer learning"
    - "neural discourse structure for text categorization"
    - "representation learning for very short texts using weighted word embedding aggregation"
    - "better document level sentiment analysis from rst discourse parsing"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-6 (induced by source paper "discovering archetypes to interpret evolution of individual behavior"):
  with members:
    - "can we count on social media metrics first insights into the active scholarly use of social media"
    - "career transitions and trajectories a case study in computing"
    - "the misleading narrative of the canonical faculty productivity trajectory"
    - "gender productivity and prestige in computer science faculty hiring networks"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"
    - "clustering hidden markov models with variational hem"
    - "analysis of computer science communities based on dblp"

Connected through hyperedge HE-7 (induced by source paper "mapping the potential and pitfalls of data dividends as a means of sharing the profits of artificial intelligence"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "towards efficient data valuation based on the shapley value"
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "low cost learning via active data procurement"
    - "incentivizing high quality crowdwork"

Connected through hyperedge HE-8 (induced by source paper "ghostlink latent network inference for influence aware recommendation"):
  with members:
    - "the author topic model for authors and documents"
    - "information diffusion and external influence in networks"
    - "uncovering the temporal dynamics of diffusion networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

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)