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: "average whenever you meet opportunistic protocols for community detection"
  Abstract: Consider the following asynchronous, opportunistic communication model over a graph $G$: in each round, one edge is activated uniformly and independently at random and (only) its two endpoints can exchange messages and perform local computations. Under this model, we study the following random process: The first time a vertex is an endpoint of an active edge, it chooses a random number, say $\pm 1$ with probability $1/2$; then, in each round, the two endpoints of the currently active edge update their values to their average. We show that, if $G$ exhibits a two-community structure (for example, two expanders connected by a sparse cut), the values held by the nodes will collectively reflect the underlying community structure over a suitable phase of the above process, allowing efficient and effective recovery in important cases. #R##N#In more detail, we first provide a first-moment analysis showing that, for a large class of almost-regular clustered graphs that includes the stochastic block model, the expected values held by all but a negligible fraction of the nodes eventually reflect the underlying cut signal. We prove this property emerges after a mixing period of length $\mathcal O(n\log n)$. We further provide a second-moment analysis for a more restricted class of regular clustered graphs that includes the regular stochastic block model. For this case, we are able to show that most nodes can efficiently and locally identify their community of reference over a suitable time window. This results in the first opportunistic protocols that approximately recover community structure using only polylogarithmic work per node. Even for the above class of regular graphs, our second moment analysis requires new concentration bounds on the product of certain random matrices that are technically challenging and possibly of independent interest.

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"
    - "on the distributed complexity of large scale graph computations"
    - "find your place simple distributed algorithms for community detection"
    - "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 "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"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "on the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-3 (induced by source paper "eigenvector computation and community detection in asynchronous gossip models"):
  with members:
    - "find your place simple distributed algorithms for community detection"
    - "a simple spectral algorithm for recovering planted partitions"
    - "gossip algorithms for distributed signal processing"

Connected through hyperedge HE-4 (induced by source paper "distributed graph clustering and sparsification"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "communication optimal distributed clustering"
    - "find your place simple distributed algorithms 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"

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.DM(Discrete Mathematics)