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: "approximating the partition function of planar two state spin systems"
  Abstract: We consider the problem of approximating the partition function of the hard-core model on planar graphs of degree at most 4. We show that when the activity λ is sufficiently large, there is no fully polynomial randomised approximation scheme for evaluating the partition function unless NP = RP . The result extends to a nearby region of the parameter space in a more general two-state spin system with three parameters. We also give a polynomial-time randomised approximation scheme for the logarithm of the partition function. We study approximation of the hard-core partition function.Our graphs are planar with degree at most 4.We show that the problem is intractable for sufficiently high activity.However, the logarithm of the partition function can be approximated.

Connected through hyperedge HE-1 (induced by source paper "detecting and counting small patterns in planar graphs in subexponential parameterized time"):
  with members:
    - "homomorphisms are a good basis for counting small subgraphs"
    - "computing the number of induced copies of a fixed graph in a bounded degree graph"
    - "subexponential parameterized algorithms for planar and apex minor free graphs via low treewidth pattern covering"
    - "counting matchings with k unmatched vertices in planar graphs"
    - "on triangulating k outerplanar graphs"
    - "approximate counting via correlation decay on planar graphs"
    - "beyond bidimensionality parameterized subexponential algorithms on directed graphs"
    - "planar subgraph isomorphism revisited"

Connected through hyperedge HE-2 (induced by source paper "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"):
  with members:
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"
    - "the complexity of approximating conservative counting csps"
    - "the complexity of ferromagnetic two spin systems with external fields"
    - "the expressibility of functions on the boolean domain with applications to counting csps"
    - "inapproximability for antiferromagnetic spin systems in the tree non uniqueness region"
    - "inapproximability after uniqueness phase transition in two spin systems"
    - "approximating the partition function of the ferromagnetic potts model"
    - "approximation algorithms for two state anti ferromagnetic spin systems on bounded degree graphs"

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)