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: "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
  Abstract: Our objective is to sample the node set of a large unknown graph via crawling, to accurately estimate a given metric of interest. We design a random walk on an appropriately defined weighted graph that achieves high efficiency by preferentially crawling those nodes and edges that convey greater information regarding the target metric. Our approach begins by employing the theory of stratification to find optimal node weights, for a given estimation problem, under an independence sampler. While optimal under independence sampling, these weights may be impractical under graph crawling due to constraints arising from the structure of the graph. Therefore, the edge weights for our random walk should be chosen so as to lead to an equilibrium distribution that strikes a balance between approximating the optimal weights under an independence sampler and achieving fast convergence. We propose a heuristic approach (stratified weighted random walk, or S-WRW) that achieves this goal, while using only limited information about the graph structure and the node properties. We evaluate our technique in simulation, and experimentally, by collecting a sample of Facebook college users. We show that S-WRW requires 13-15 times fewer samples than the simple re-weighted random walk (RW) to achieve the same estimation accuracy for a range of metrics.

Connected through hyperedge HE-1 (induced by source paper "star sampling with and without replacement"):
  with members:
    - "a markov chain model for the search time for max degree nodes in a graph using a biased random walk"
    - "a survey and taxonomy of graph sampling"
    - "quick detection of nodes with large degrees"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "search in power law networks"

Connected through hyperedge HE-2 (induced by source paper "estimating group properties in online social networks with a classifier"):
  with members:
    - "white man and highly followed gender and race inequalities in twitter"
    - "sampling from social networks with attributes"
    - "you are what apps you use demographic prediction based on user s apps"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-3 (induced by source paper "enhancing stratified graph sampling algorithms based on approximate degree distribution"):
  with members:
    - "effect of different distance measures on the performance of k means algorithm an experimental study in matlab"
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-4 (induced by source paper "one step estimation of networked population size respondent driven capture recapture with anonymity"):
  with members:
    - "efficiently estimating motif statistics of large networks"
    - "graph size estimation"

Connected through hyperedge HE-5 (induced by source paper "inferring structural characteristics of networks with strong and weak ties from fixed choice surveys"):
  with members:
    - "the role of social networks in information diffusion"
    - "social contagion theory examining dynamic social networks and human behavior"
    - "estimating and sampling graphs with multidimensional random walks"
    - "what is the real size of a sampled network the case of the internet"

Connected through hyperedge HE-6 (induced by source paper "generating online social networks based on socio demographic attributes"):
  with members:
    - "demographic and structural characteristics to rationalize link formation in online social networks"
    - "tunable and growing network generation model with community structures"
    - "social structure of facebook networks"

Connected through hyperedge HE-7 (induced by source paper "efficient sampling for better osn data provisioning"):
  with members:
    - "estimating and sampling graphs with multidimensional random walks"
    - "stream sampling for variance optimal estimation of subset sums"
    - "social networks that matter twitter under the microscope"

Connected through hyperedge HE-8 (induced by source paper "leveraging history for faster sampling of online social networks"):
  with members:
    - "faster random walks by rewiring online social networks on the fly"
    - "defining and evaluating network communities based on ground truth"
    - "estimating and sampling graphs with multidimensional random walks"

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)