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: "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
  Abstract: Food and drink are two of the most basic needs of human beings. However, as society evolved, food and drink became also a strong cultural aspect, being able to describe strong differences among people. Traditional methods used to analyze cross-cultural differences are mainly based on surveys and, for this reason, they are very difficult to represent a significant statistical sample at a global scale. In this paper, we propose a new methodology to identify cultural boundaries and similarities across populations at different scales based on the analysis of Foursquare check-ins. This approach might be useful not only for economic purposes, but also to support existing and novel marketing and social applications. Our methodology consists of the following steps. First, we map food and drink related check-ins extracted from Foursquare into users' cultural preferences. Second, we identify particular individual preferences, such as the taste for a certain type of food or drink, e.g., pizza or sake, as well as temporal habits, such as the time and day of the week when an individual goes to a restaurant or a bar. Third, we show how to analyze this information to assess the cultural distance between two countries, cities or even areas of a city. Fourth, we apply a simple clustering technique, using this cultural distance measure, to draw cultural boundaries across countries, cities and regions.

Connected through hyperedge HE-1 (induced by source paper "towards business partnership recommendation using user opinion on facebook"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "gender matters analyzing global cultural gender preferences for venues using social sensing"
    - "chatty maps constructing sound maps of urban areas from social media data"
    - "where is the goldmine finding promising business locations through facebook data analytics"
    - "geo spotting mining online location based services for optimal retail store placement"

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"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"
    - "mining cross cultural relations from wikipedia a study of 31 european food cultures"

Connected through hyperedge HE-4 (induced by source paper "of wines and reviews measuring and modeling the vivino wine social network"):
  with members:
    - "fetishizing food in digital age foodporn around the world"
    - "kissing cuisines exploring worldwide culinary habits on the web"
    - "foodporn obesity patterns in culinary interactions"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-5 (induced by source paper "you are your metadata identification and obfuscation of social media users using metadata information"):
  with members:
    - "mobile device identification via sensor fingerprinting"
    - "scikit learn machine learning in python"
    - "social turing tests crowdsourcing sybil detection"
    - "de anonymizing social networks"

Connected through hyperedge HE-6 (induced by source paper "predicting the temporal activity patterns of new venues"):
  with members:
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-7 (induced by source paper "sensing and modeling human behavior using social media and mobile data"):
  with members:
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "hoodsquare modeling and recommending neighborhoods in location based social networks"
    - "predicting influential users in online social networks"

Connected through hyperedge HE-8 (induced by source paper "gender matters analyzing global cultural gender preferences for venues using social sensing"):
  with members:
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "first women second sex gender bias in wikipedia"
    - "the call of the crowd event participation in location based social services"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "uncovering social network sybils in the wild"

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)