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: "language in our time an empirical analysis of hashtags"
  Abstract: Hashtags in online social networks have gained tremendous popularity during the past five years. The resulting large quantity of data has provided a new lens into modern society. Previously, researchers mainly rely on data collected from Twitter to study either a certain type of hashtags or a certain property of hashtags. In this paper, we perform the first large-scale empirical analysis of hashtags shared on Instagram, the major platform for hashtag-sharing. We study hashtags from three different dimensions including the temporal-spatial dimension, the semantic dimension, and the social dimension. Extensive experiments performed on three large-scale datasets with more than 7 million hashtags in total provide a series of interesting observations. First, we show that the temporal patterns of hashtags can be categorized into four different clusters, and people tend to share fewer hashtags at certain places and more hashtags at others. Second, we observe that a non-negligible proportion of hashtags exhibit large semantic displacement. We demonstrate hashtags that are more uniformly shared among users, as quantified by the proposed hashtag entropy, are less prone to semantic displacement. In the end, we propose a bipartite graph embedding model to summarize users' hashtag profiles, and rely on these profiles to perform friendship prediction. Evaluation results show that our approach achieves an effective prediction with AUC (area under the ROC curve) above 0.8 which demonstrates the strong social signals possessed in hashtags.

Connected through hyperedge HE-1 (induced by source paper "everything about you a multimodal approach towards friendship inference in online social networks"):
  with members:
    - "tagvisor a privacy advisor for sharing hashtags"
    - "you are your metadata identification and obfuscation of social media users using metadata information"
    - "graph embedding techniques applications and performance a survey"
    - "a hybrid model for identity obfuscation by face replacement"
    - "attriguard a practical defense against attribute inference attacks via adversarial machine learning"
    - "friendships rivalries and trysts characterizing relations between ideas in texts"
    - "connecting pixels to privacy and utility automatic redaction of private information in images"
    - "walk2friends inferring social links from mobility profiles"

Connected through hyperedge HE-2 (induced by source paper "how much and when do we need higher order information in hypergraphs a case study on hyperedge prediction"):
  with members:
    - "hypergraph neural networks"
    - "simplicial closure and higher order link prediction"
    - "is saki delicious the food perception gap on instagram and its relation to health"
    - "node2vec scalable feature learning for networks"
    - "uniform hypergraph partitioning provable tensor methods and sampling techniques"
    - "learning hypergraph regularized attribute predictors"
    - "predicting multi actor collaborations using hypergraphs"

Connected through hyperedge HE-3 (induced by source paper "towards plausible graph anonymization"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "tagvisor a privacy advisor for sharing hashtags"
    - "privacy in social media identification mitigation and applications"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "walk2friends inferring social links from mobility profiles"
    - "evolution of ego networks in social media with link recommendations"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "hashtags are not judgemental the untold story of lok sabha elections 2019"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "diachronic word embeddings reveal statistical laws of semantic change"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)