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: "mining cross cultural relations from wikipedia a study of 31 european food cultures"
  Abstract: For many people, Wikipedia represents one of the primary sources of knowledge about foreign cultures. Yet, different Wikipedia language editions offer different descriptions of cultural practices. Unveiling diverging representations of cultures provides an important insight, since they may foster the formation of cross-cultural stereotypes, misunderstandings and potentially even conflict. In this work, we explore to what extent the descriptions of cultural practices in various European language editions of Wikipedia differ on the example of culinary practices and propose an approach to mine cultural relations between different language communities trough their description of and interest in their own and other communities' food culture. We assess the validity of the extracted relations using 1) various external reference data sources (i.e., the European Social Survey, migration statistics), 2) crowdsourcing methods and 3) simulations.

Connected through hyperedge HE-1 (induced by source paper "what is trending on wikipedia capturing trends and language biases across wikipedia editions"):
  with members:
    - "a graph structured dataset for wikipedia research"
    - "why the world reads wikipedia beyond english speakers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"

Connected through hyperedge HE-2 (induced by source paper "a survey on food computing"):
  with members:
    - "food recommendation framework existing solutions and challenges"
    - "learning cross modal embeddings with adversarial networks for cooking recipes and food images"
    - "recognition of multiple food items in a single photo for use in a buffet style restaurant"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "scaling egocentric vision the epic kitchens dataset"
    - "rice classification using spatio spectral deep convolutional neural network"
    - "does journaling encourage healthier choices analyzing healthy eating behaviors of food journalers"
    - "single view food portion estimation learning image to energy mappings using generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "why the world reads wikipedia beyond english speakers"):
  with members:
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "what makes a link successful on wikipedia"
    - "measuring urban deprivation from user generated content"
    - "cultures in community question answering"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"

Connected through hyperedge HE-4 (induced by source paper "world literature according to wikipedia introduction to a dbpedia based framework"):
  with members:
    - "cultural anthropology through the lens of wikipedia a comparison of historical leadership networks in the english chinese japanese and german wikipedia"
    - "bots vs wikipedians anons vs logged ins"
    - "biographical social networks on wikipedia a cross cultural study of links that made history"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"
    - "mining meaning from wikipedia"

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)