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: "find your place simple distributed algorithms for community detection"
  Abstract: Given an underlying graph, we consider the following \emph{dynamics}: Initially, each node locally chooses a value in $\{-1,1\}$, uniformly at random and independently of other nodes. Then, in each consecutive round, every node updates its local value to the average of the values held by its neighbors, at the same time applying an elementary, local clustering rule that only depends on the current and the previous values held by the node. #R##N#We prove that the process resulting from this dynamics produces a clustering that exactly or approximately (depending on the graph) reflects the underlying cut in logarithmic time, under various graph models that exhibit a sparse balanced cut, including the stochastic block model. We also prove that a natural extension of this dynamics performs community detection on a regularized version of the stochastic block model with multiple communities. #R##N#Rather surprisingly, our results provide rigorous evidence for the ability of an extremely simple and natural dynamics to address a computational problem that is non-trivial even in a centralized setting.

Connected through hyperedge HE-1 (induced by source paper "efficient distributed community detection in the stochastic block model"):
  with members:
    - "large scale distributed algorithms for facility location with outliers"
    - "local mixing time distributed computation and applications"
    - "near optimal clustering in the k machine model"
    - "average whenever you meet opportunistic protocols for community detection"
    - "on the distributed complexity of large scale graph computations"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-2 (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"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "distributed graph clustering and sparsification"
    - "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-3 (induced by source paper "understanding filter bubbles and polarization in social networks"):
  with members:
    - "opinion dynamics with varying susceptibility to persuasion"
    - "eigenvector computation and community detection in asynchronous gossip models"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "maximizing the diversity of exposure in a social network"
    - "opinion maximization in social networks"
    - "biased assimilation homophily and the dynamics of polarization"
    - "how bad is forming your own opinion"

Connected through hyperedge HE-4 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-5 (induced by source paper "eigenvector computation and community detection in asynchronous gossip models"):
  with members:
    - "average whenever you meet opportunistic protocols for community detection"
    - "a simple spectral algorithm for recovering planted partitions"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-6 (induced by source paper "distributed graph clustering and sparsification"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "communication optimal distributed clustering"
    - "average whenever you meet opportunistic protocols for community detection"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

Connected through hyperedge HE-7 (induced by source paper "block models and personalized pagerank"):
  with members:
    - "community detection in networks with unequal groups"
    - "heat kernel based community detection"
    - "streaming graph partitioning in the planted partition model"
    - "pagerank beyond the web"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-8 (induced by source paper "distributed graph clustering by load balancing"):
  with members:
    - "communication optimal distributed clustering"
    - "partitioning well clustered graphs spectral clustering works"
    - "partitioning into expanders"
    - "quasirandom load balancing"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "tight bounds for randomized load balancing on arbitrary network topologies"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"

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)