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: "graph cluster randomization network exposure to multiple universes"
  Abstract: A/B testing is a standard approach for evaluating the effect of online experiments; the goal is to estimate the `average treatment effect' of a new feature or condition by exposing a sample of the overall population to it. A drawback with A/B testing is that it is poorly suited for experiments involving social interference, when the treatment of individuals spills over to neighboring individuals along an underlying social network. In this work, we propose a novel methodology using graph clustering to analyze average treatment effects under social interference. To begin, we characterize graph-theoretic conditions under which individuals can be considered to be `network exposed' to an experiment. We then show how graph cluster randomization admits an efficient exact algorithm to compute the probabilities for each vertex being network exposed under several of these exposure conditions. Using these probabilities as inverse weights, a Horvitz-Thompson estimator can then provide an effect estimate that is unbiased, provided that the exposure model has been properly specified. #R##N#Given an estimator that is unbiased, we focus on minimizing the variance. First, we develop simple sufficient conditions for the variance of the estimator to be asymptotically small in n, the size of the graph. However, for general randomization schemes, this variance can be lower bounded by an exponential function of the degrees of a graph. In contrast, we show that if a graph satisfies a restricted-growth condition on the growth rate of neighborhoods, then there exists a natural clustering algorithm, based on vertex neighborhoods, for which the variance of the estimator can be upper bounded by a linear function of the degrees. Thus we show that proper cluster randomization can lead to exponentially lower estimator variance when experimentally measuring average treatment effects under interference.

Connected through hyperedge HE-1 (induced by source paper "inferring individual level causal models from graph based relational time series"):
  with members:
    - "designing and deploying online field experiments"
    - "a dynamical system for pagerank with time dependent teleportation"
    - "inferring networks of diffusion and influence"

Connected through hyperedge HE-2 (induced by source paper "randomized experimental design via geographic clustering"):
  with members:
    - "distributed balanced partitioning via linear embedding"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-3 (induced by source paper "local algorithms for hierarchical dense subgraph discovery"):
  with members:
    - "shared memory graph truss decomposition"
    - "estimation of graphlet statistics"
    - "static graph challenge subgraph isomorphism"
    - "higher order organization of complex networks"
    - "locally estimating core numbers"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "parallel peeling algorithms"
    - "truss decomposition in massive networks"

Connected through hyperedge HE-4 (induced by source paper "randomized experiments to detect and estimate social influence in networks"):
  with members:
    - "discussion quality diffuses in the digital public square"
    - "designing and deploying online field experiments"
    - "selection effects in online sharing consequences for peer adoption"
    - "the role of social networks in information diffusion"
    - "social influence in social advertising evidence from field experiments"
    - "cooperation and contagion in web based networked public goods experiments"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-5 (induced by source paper "influence estimation on social media networks using causal inference"):
  with members:
    - "online human bot interactions detection estimation and characterization"
    - "bayesian discovery of threat networks"

Connected through hyperedge HE-6 (induced by source paper "hellrank a hellinger based centrality measure for bipartite social networks"):
  with members:
    - "distributed algorithms for computation of centrality measures in complex networks"
    - "extracting implicit social relation for social recommendation techniques in user rating prediction"
    - "user recommendations in reciprocal and bipartite social networks an online dating case study"
    - "identifying influential nodes in bipartite networks using the clustering coefficient"
    - "network sampling from static to streaming graphs"
    - "benefits of bias towards better characterization of network sampling"

Connected through hyperedge HE-7 (induced by source paper "geocuts geographic clustering using travel statistics"):
  with members:
    - "distributed balanced partitioning via linear embedding"
    - "finding connected components on map reduce in logarithmic rounds"

Connected through hyperedge HE-8 (induced by source paper "coloring large complex networks"):
  with members:
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "graph sample and hold a framework for big graph analytics"
    - "estimating clique composition and size distributions from sampled network data"
    - "dimension reduction via colour refinement"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "network sampling from static to streaming graphs"
    - "exact algorithms for maximum clique a computational study"
    - "exact algorithms for maximum clique a computational study"

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)