human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the complexity of approximating conservative counting csps"):
  Abstract: We study the complexity of the approximate weighted counting constraint satisfaction problem #CSP ( F ) . In the conservative case, where F contains all unary functions, a classification is known over the Boolean domain; we extend this to arbitrary finite domains. We show that if F is "weakly log-modular", then #CSP ( F ) is in FP. Otherwise, it is at least as difficult to approximate as #BIS (counting independent sets in bipartite graphs). #BIS is complete for the complexity class # R H ? 1 , and believed to be intractable. We further sub-divide the #BIS-hard case: if F is "weakly log-supermodular", #CSP ( F ) is as easy as a Boolean log-supermodular weighted #CSP; otherwise, we show that it is NP-hard to approximate. Finally, we give a full trichotomy for the arity-2 case: #CSP ( F ) is in FP, is #BIS-equivalent, or is equivalent to #SAT (approximately counting the satisfying assignments of Boolean CNF formulas). We also discuss algorithmic aspects of our classification. We classify the complexity of approximating conservative weighted counting CSP.It is in FP, as hard as counting bipartite independent sets (#BIS) or as hard as #SAT.For functions of arity 2, the #BIS-hard case is equivalent to #BIS.For higher arity, the #BIS-hard case reduces to a Boolean weighted #CSP or is NP-hard.Given a weighted constraint language, the classification is decidable.

Includes members:
  Member "the complexity of conservative valued csps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing fitness landscapes by valued constraints to understand the complexity of local search")
  Member "the expressibility of functions on the boolean domain with applications to counting csps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models")
  Member "the complexity of weighted and unweighted csp":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity of counting weighted eulerian orientations with ars")
  Member "approximating the partition function of the ferromagnetic potts model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lower bounds for testing graphical models colorings and antiferromagnetic ising models")
  Member "complexity of counting csp with complex weights":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal polynomial time compression for boolean max csp")
  Member "a maximal tractable class of soft constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binarisation for valued constraint satisfaction problems")
  Member "approximate counting for complex weighted boolean constraint satisfaction problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "holant clones and the approximability of conservative holant problems")
  Member "an effective dichotomy for the counting constraint satisfaction problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximate counting csp seen from the other side")

Question: Please tell me which class the source paper 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)