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: "measuring tie strength in implicit social networks"
  Abstract: Given a set of people and a set of events they attend, we address the problem of measuring connectedness or tie strength between each pair of persons given that attendance at mutual events gives an implicit social network between people. We take an axiomatic approach to this problem. Starting from a list of axioms that a measure of tie strength must satisfy, we characterize functions that satisfy all the axioms and show that there is a range of measures that satisfy this characterization. A measure of tie strength induces a ranking on the edges (and on the set of neighbors for every person). We show that for applications where the ranking, and not the absolute value of the tie strength, is the important thing about the measure, the axioms are equivalent to a natural partial order. Also, to settle on a particular measure, we must make a non-obvious decision about extending this partial order to a total order, and that this decision is best left to particular applications. We classify measures found in prior literature according to the axioms that they satisfy. In our experiments, we measure tie strength and the coverage of our axioms in several datasets. Also, for each dataset, we bound the maximum Kendall's Tau divergence (which measures the number of pairwise disagreements between two lists) between all measures that satisfy the axioms using the partial order. This informs us if particular datasets are well behaved where we do not have to worry about which measure to choose, or we have to be careful about the exact choice of measure we make.

Connected through hyperedge HE-1 (induced by source paper "link classification and tie strength ranking in online social networks with exogenous interaction networks"):
  with members:
    - "detecting strong ties using network motifs"
    - "link prediction in social networks the state of the art"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "community structure and interaction dynamics through the lens of quotes"):
  with members:
    - "quotus the structure of political media coverage as revealed by quoting patterns"

Connected through hyperedge HE-3 (induced by source paper "graph based anomaly detection and description a survey"):
  with members:
    - "detecting change points in the large scale structure of evolving networks"
    - "deltacon a principled massive graph similarity function"
    - "discriminative probabilistic models for relational data"
    - "role dynamics fast mining of large dynamic networks"
    - "estimating the prevalence of deception in online review communities"
    - "identifying the signs of fraudulent accounts using data mining techniques"
    - "netsimile a scalable approach to size independent network similarity"
    - "finding deceptive opinion spam by any stretch of the imagination"

Connected through hyperedge HE-4 (induced by source paper "the power of indirect social ties"):
  with members:
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-5 (induced by source paper "social resilience in online communities the autopsy of friendster"):
  with members:
    - "on the bursty evolution of online social networks"
    - "modeling the adoption of innovations in the presence of geographic and media influences"
    - "defining and evaluating network communities based on ground truth"
    - "partisan asymmetries in online political activity"

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)