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: "eigenvector computation and community detection in asynchronous gossip models"
  Abstract: We give a simple distributed algorithm for computing adjacency matrix eigenvectors for the communication graph in an asynchronous gossip model. We show how to use this algorithm to give state-of-the-art asynchronous community detection algorithms when the communication graph is drawn from the well-studied stochastic block model. Our methods also apply to a natural alternative model of randomized communication, where nodes within a community communicate more frequently than nodes in different communities. Our analysis simplifies and generalizes prior work by forging a connection between asynchronous eigenvector computation and Oja's algorithm for streaming principal component analysis. We hope that our work serves as a starting point for building further connections between the analysis of stochastic iterative methods, like Oja's algorithm, and work on asynchronous and gossip-type algorithms for distributed computation.

Connected through hyperedge HE-1 (induced by source paper "step by step community detection for volume regular graphs"):
  with members:
    - "distributed community detection via metastability of the 2 choices dynamics"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "distributed graph clustering and sparsification"
    - "find your place simple distributed algorithms for community detection"
    - "partitioning well clustered graphs spectral clustering works"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "on the analysis of a label propagation algorithm for community detection"

Connected through hyperedge HE-2 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "opinion dynamics with varying susceptibility to persuasion"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "maximizing the diversity of exposure in a social network"
    - "find your place simple distributed algorithms for community detection"
    - "opinion maximization in social networks"
    - "biased assimilation homophily and the dynamics of polarization"
    - "how bad is forming your own opinion"

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.DC(Distributed, Parallel, and Cluster Computing)