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: "sampling from social networks with attributes"
  Abstract: Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.

Connected through hyperedge HE-1 (induced by source paper "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures"):
  with members:
    - "blind inference of centrality rankings from graph signals"
    - "towards quantifying sampling bias in network inference"
    - "node2vec scalable feature learning for networks"
    - "stability and continuity of centrality measures in weighted graphs"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-2 (induced by source paper "a total error framework for digital traces of humans"):
  with members:
    - "model cards for model reporting"
    - "demographic inference and representative population estimates from multilingual social media data"
    - "can you trust the trend discovering simpson s paradoxes in social data"
    - "how constraints affect content the case of twitter s switch from 140 to 280 characters"
    - "datasheets for datasets"
    - "false information on web and social media a survey"
    - "systematically monitoring social media the case of the german federal election 2017"
    - "using social media to predict the future a systematic literature review"

Connected through hyperedge HE-3 (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"
    - "you are what apps you use demographic prediction based on user s apps"
    - "walking on a graph with a magnifying glass stratified sampling via weighted random walks"
    - "estimating and sampling graphs with multidimensional random walks"

Connected through hyperedge HE-4 (induced by source paper "towards quantifying sampling bias in network inference"):
  with members:
    - "social structure of facebook networks"

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)