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: "sequential voting promotes collective discovery in social recommendation systems"
  Abstract: One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this question have been limited by a lack of objective measurements of content quality. Quality is typically defined endogenously as the popularity of content in absence of social influence. The flaw of this metric is its presupposition that the preferences of the crowd are aligned with underlying quality. Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems. In this work, we look to the domain of education, where content quality can be measured via how well students are able to learn from the material presented to them. Through a behavioral experiment involving a simulated massive open online course (MOOC) run on Amazon Mechanical Turk, we show that sequential voting systems can surface better content than systems that elicit independent votes.

Connected through hyperedge HE-1 (induced by source paper "quantifying voter biases in online platforms an instrumental variable approach"):
  with members:
    - "quantifying the impact of cognitive biases in question answering systems"
    - "the size conundrum why online knowledge markets can fail at scale"
    - "guessthekarma a game to assess social rating systems"
    - "when sheep shop measuring herding effects in product ratings with natural experiments"
    - "caveat emptor computational social science large scale missing data in a widely published reddit corpus"
    - "consumers and curators browsing and voting patterns on reddit"
    - "rating effects on social news posts and comments"
    - "unbiased learning to rank with biased feedback"

Connected through hyperedge HE-2 (induced by source paper "quantifying the impact of cognitive biases in question answering systems"):
  with members:
    - "dynamics of content quality in collaborative knowledge production"
    - "information overload in group communication from conversation to cacophony in the twitch chat"
    - "the myopia of crowds a study of collective evaluation on stack exchange"
    - "random voting effects in social digital spaces a case study of reddit post submissions"

Connected through hyperedge HE-3 (induced by source paper "guessthekarma a game to assess social rating systems"):
  with members:
    - "consumers and curators browsing and voting patterns on reddit"
    - "rating effects on social news posts and comments"
    - "the myopia of crowds a study of collective evaluation on stack exchange"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "crowdsourced judgement elicitation with endogenous proficiency"

Connected through hyperedge HE-4 (induced by source paper "ranking with fairness constraints"):
  with members:
    - "fair and diverse dpp based data summarization"
    - "multiwinner voting with fairness constraints"
    - "inherent trade offs in the fair determination of risk scores"
    - "on the complexity of constrained determinantal point processes"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "fairness through awareness"

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)