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: "a walk in facebook uniform sampling of users in online social networks"
  Abstract: Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph. Such a sample allows to estimate any user property and some topological properties as well. To this end, first, we consider and compare several candidate crawling techniques. Two approaches that can produce approximately uniform samples are the Metropolis-Hasting random walk (MHRW) and a re-weighted random walk (RWRW). Both have pros and cons, which we demonstrate through a comparison to each other as well as to the "ground truth." In contrast, using Breadth-First-Search (BFS) or an unadjusted Random Walk (RW) leads to substantially biased results. Second, and in addition to offline performance assessment, we introduce online formal convergence diagnostics to assess sample quality during the data collection process. We show how these diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality. Third, as a case study, we apply the above methods to Facebook and we collect the first, to the best of our knowledge, representative sample of Facebook users. We make it publicly available and employ it to characterize several key properties of Facebook.

Connected through hyperedge HE-1 (induced by source paper "fairness in online social network timelines measurements models and mechanism design"):
  with members:
    - "biases in the facebook news feed a case study on the italian elections"
    - "do diffusion protocols govern cascade growth"
    - "closing the ai knowledge gap"
    - "fairness of exposure in rankings"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "alpha fair coded caching"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-2 (induced by source paper "ant inspired density estimation via random walks"):
  with members:
    - "limits for rumor spreading in stochastic populations"
    - "on coalescence time in graphs when is coalescing as fast as meeting"
    - "graph size estimation"

Connected through hyperedge HE-3 (induced by source paper "benefits of bias towards better characterization of network sampling"):
  with members:
    - "expansion and search in networks"
    - "on the bias of bfs"
    - "search in power law networks"

Connected through hyperedge HE-4 (induced by source paper "characterizing continuous time random walks on time varying graphs"):
  with members:
    - "estimating and sampling graphs with multidimensional random walks"
    - "modeling and analysis of time varying graphs"

Connected through hyperedge HE-5 (induced by source paper "nonadaptive mastermind algorithms for string and vector databases with case studies"):
  with members:
    - "on the algorithmic complexity of the mastermind game with black peg results"
    - "the mastermind attack on genomic data"
    - "de anonymizing social networks"

Connected through hyperedge HE-6 (induced by source paper "link creation and profile alignment in the anobii social network"):
  with members:
    - "folks in folksonomies social link prediction from shared metadata"

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)