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: "spatial mixing and approximate counting for potts model on graphs with bounded average degree"
  Abstract: We propose a notion of contraction function for a family of graphs and establish its connection to the strong spatial mixing for spin systems. More specifically, we show that for anti-ferromagnetic Potts model on families of graphs characterized by a specific contraction function, the model exhibits strong spatial mixing, and if further the graphs exhibit certain local sparsity which are very natural and easy to satisfy by typical sparse graphs, then we also have FPTAS for computing the partition function. #R##N#This new characterization of strong spatial mixing of multi-spin system does not require maximum degree of the graphs to be bounded, but instead it relates the decay of correlation of the model to a notion of effective average degree measured by the contraction of a function on the family of graphs. It also generalizes other notion of effective average degree which may determine the strong spatial mixing, such as the connective constant, whose connection to strong spatial mixing is only known for very simple models and is not extendable to general spin systems. #R##N#As direct consequences: (1) we obtain FPTAS for the partition function of $q$-state anti-ferromagnetic Potts model with activity $0\le\beta  3(1-\beta)d+1$, improving the previous best bound $\beta> 3(1-\beta)d$ and asymptotically approaching the inapproximability threshold $q=(1-\beta)d$, and (2) we obtain an efficient sampler (in the same sense of fully polynomial-time almost uniform sampler, FPAUS) for the Potts model on Erd\H{o}s-R\'enyi random graph $\mathcal{G}(n,d/n)$ with sufficiently large constant $d$, provided that $q> 3(1-\beta)d+4$. In particular when $\beta=0$, the sampler becomes an FPAUS for for proper $q$-coloring in $\mathcal{G}(n,d/n)$ with $q> 3d+4$, improving the current best bound $q> 5.5d$ for FPAUS for $q$-coloring in $\mathcal{G}(n,d/n)$.

Connected through hyperedge HE-1 (induced by source paper "approximation via correlation decay when strong spatial mixing fails"):
  with members:
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "fptas for hardcore and ising models on hypergraphs"
    - "the complexity of approximately counting in 2 spin systems on k uniform bounded degree hypergraphs"
    - "approximately counting locally optimal structures"
    - "spatial mixing and the connective constant optimal bounds"
    - "fptas for counting monotone cnf"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"
    - "correlation decay up to uniqueness in spin systems"

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)