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: "equality of voice towards fair representation in crowdsourced top k recommendations"
  Abstract: To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. #R##N#To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.

Connected through hyperedge HE-1 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-2 (induced by source paper "on the fairness of time critical influence maximization in social networks"):
  with members:
    - "group fairness in influence maximization"
    - "learning optimal and fair decision trees for non discriminative decision making"
    - "gaps in information access in social networks"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "fair allocation of indivisible public goods"
    - "approximate maximin shares for groups of agents"
    - "multiwinner elections with diversity constraints"
    - "democratic fair allocation of indivisible goods"

Connected through hyperedge HE-3 (induced by source paper "incremental fairness in two sided market platforms on updating recommendations fairly"):
  with members:
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "multi stakeholder recommendation and its connection to multi sided fairness"
    - "equity of attention amortizing individual fairness in rankings"
    - "translation based recommendation"
    - "multisided fairness for recommendation"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "empirical analysis of predictive algorithms for collaborative filtering"

Connected through hyperedge HE-4 (induced by source paper "modelling bahdanau attention using election methods aided by q learning"):
  with members:
    - "attention is all you need"
    - "massive exploration of neural machine translation architectures"
    - "word translation without parallel data"
    - "effective approaches to attention based neural machine translation"
    - "properties of multiwinner voting rules"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-5 (induced by source paper "public sphere 2 0 targeted commenting in online news media"):
  with members:
    - "tabloids in the era of social media understanding the production and consumption of clickbaits in twitter"
    - "who makes trends understanding demographic biases in crowdsourced recommendations"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "gerrymandering a briber s perspective"):
  with members:
    - "how hard is bribery in elections"
    - "anyone but him the complexity of precluding an alternative"

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)