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: "the switch markov chain for sampling irregular graphs"
  Abstract: The problem of efficiently sampling from a set of(undirected) graphs with a given degree sequence has many applications. One approach to this problem uses a simple Markov chain, which we call the switch chain, to perform the sampling. The switch chain is known to be rapidly mixing for regular degree sequences. We prove that the switch chain is rapidly mixing for any degree sequence with minimum degree at least 1 and with maximum degree $d_{\max}$ which satisfies $3\leq d_{\max}\leq \frac{1}{4}\, \sqrt{M}$, where $M$ is the sum of the degrees. The mixing time bound obtained is only an order of $n$ larger than that established in the regular case, where $n$ is the number of vertices.

Connected through hyperedge HE-1 (induced by source paper "moments of uniform random multigraphs with fixed degree sequences"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "graphic realizations of joint degree matrices"
    - "network clustering via maximizing modularity approximation algorithms and theoretical limits"

Connected through hyperedge HE-2 (induced by source paper "measuring directed triadic closure with closure coefficients"):
  with members:
    - "directed closure measures for networks with reciprocity"
    - "friendship networks and social status"
    - "community structure and scale free collections of erdős renyi graphs"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "expanders via local edge flips"):
  with members:
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "tight bounds for rumor spreading with vertex expansion"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "tight bounds for randomized load balancing on arbitrary network topologies"
    - "simple fast and deterministic gossip and rumor spreading"
    - "dex self healing expanders"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-4 (induced by source paper "a survey of discrete methods in algebraic statistics for networks"):
  with members:
    - "ergms are hard"
    - "asymptotic equivalence of bayes cross validation and widely applicable information criterion in singular learning theory"

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.DS(Data Structures and Algorithms)