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 weighted correlation index for rankings with ties"
  Abstract: Understanding the correlation between two different scores for the same set of items is a common problem in information retrieval, and the most commonly used statistics that quantifies this correlation is Kendall's $\tau$. However, the standard definition fails to capture that discordances between items with high rank are more important than those between items with low rank. Recently, a new measure of correlation based on average precision has been proposed to solve this problem, but like many alternative proposals in the literature it assumes that there are no ties in the scores. This is a major deficiency in a number of contexts, and in particular while comparing centrality scores on large graphs, as the obvious baseline, indegree, has a very large number of ties in web and social graphs. We propose to extend Kendall's definition in a natural way to take into account weights in the presence of ties. We prove a number of interesting mathematical properties of our generalization and describe an $O(n\log n)$ algorithm for its computation. We also validate the usefulness of our weighted measure of correlation using experimental data.

Connected through hyperedge HE-1 (induced by source paper "micro and macro level churn analysis of large scale mobile games"):
  with members:
    - "a semi supervised and inductive embedding model for churn prediction of large scale mobile games"
    - "streaming network embedding through local actions"
    - "learning edge representations via low rank asymmetric projections"
    - "semi supervised embedding in attributed networks with outliers"
    - "customer lifetime value prediction using embeddings"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"

Connected through hyperedge HE-2 (induced by source paper "a cryptoeconomic traffic analysis of bitcoins lightning network"):
  with members:
    - "privacy utility tradeoffs in routing cryptocurrency over payment channel networks"
    - "topological analysis of bitcoin s lightning network"
    - "the evolution of lightning network s topology during its first year and the influence over its core values"
    - "payment networks as creation games"
    - "discharged payment channels quantifying the lightning network s resilience to topology based attacks"
    - "ant routing algorithm for the lightning network"
    - "payment network design with fees"
    - "towards an economic analysis of routing in payment channel networks"

Connected through hyperedge HE-3 (induced by source paper "quantifying consensus of rankings based on q support patterns"):
  with members:
    - "maximal rank correlation"
    - "optimizing positional scoring rules for rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "maximal rank correlation"
    - "a family of rank similarity measures based on maximized effectiveness difference"

Connected through hyperedge HE-4 (induced by source paper "graph based selective outlier ensembles"):
  with members:
    - "learning representations for outlier detection on a budget"

Connected through hyperedge HE-5 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection"):
  with members:
    - "rhetorical relations for information retrieval"
    - "part of speech based term weighting for information retrieval"
    - "maximal rank correlation"
    - "maximal rank correlation"
    - "non compositional term dependence for information retrieval"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "a network model characterized by a latent attribute structure with competition"):
  with members:
    - "nonparametric link prediction in dynamic networks"
    - "an infinite latent attribute model for network data"
    - "infinite multiple membership relational modeling for complex networks"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"

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)