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: "counting independent sets in hypergraphs when strong spatial mixing fails"
  Abstract: We study the problem of approximately counting independent sets in hypergraphs with maximum degree $\Delta$ whose hyperedges have arity at least $k\geq 3$. In the graphical case, where the arity of every edge is $2$, it is known that for $\Delta \geq 6$ the problem is hard and for $\Delta \leq 5$ there is an FPTAS. This approximation algorithm uses correlation decay techniques that rely on the fact that strong spatial mixing occurs. #R##N#Surprisingly, in the hypergraph case we give a correlation decay based algorithm even though strong spatial mixing fails. Our main idea is introducing amortization in the correlation decay proof. In particular, in the correlation decay proof we track not just the decay but also combinatorial properties of the intermediate instances. This enables us to give an FPTAS even when strong spatial mixing fails, for example, when $\Delta=6$ and $k \geq 3$ and also for all $\Delta$ and all sufficiently large $k\geq 1.66\Delta$. We further demonstrate that in the hypergraph independent set model, approximating the partition function is NP-hard even within the uniqueness regime.

Connected through hyperedge HE-1 (induced by source paper "counting hypergraph matchings up to uniqueness threshold"):
  with members:
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "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"
    - "approximation via correlation decay when strong spatial mixing fails"
    - "fptas for weighted fibonacci gates and its applications"
    - "a simple fptas for counting edge covers"
    - "spatial mixing and the connective constant optimal bounds"

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.CC(Computational Complexity)