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 complexity of conservative valued csps"
  Abstract: We study the complexity of valued constraint satisfaction problems (VCSPs) parametrized by a constraint language, a fixed set of cost functions over a finite domain. An instance of the problem is specified by a sum of cost functions from the language and the goal is to minimize the sum. Under the unique games conjecture, the approximability of finite-valued VCSPs is well understood, see Raghavendra [2008]. However, there is no characterization of finite-valued VCSPs, let alone general-valued VCSPs, that can be solved exactly in polynomial time, thus giving insights from a combinatorial optimization perspective.   We consider the case of languages containing all possible unary cost functions. In the case of languages consisting of only {0,∞}-valued cost functions (i.e., relations), such languages have been called conservative and studied by Bulatov [2003, 2011] and recently by Barto [2011]. Since we study valued languages, we call a language conservative if it contains all finite-valued unary cost functions. The computational complexity of conservative valued languages has been studied by Cohen et al. [2006] for languages over Boolean domains, by Deineko et al. [2008] for {0,1}-valued languages (a.k.a Max-CSP), and by Takhanov [2010a] for {0,∞}-valued languages containing all finite-valued unary cost functions (a.k.a. Min-Cost-Hom).   We prove a Schaefer-like dichotomy theorem for conservative valued languages: if all cost functions in the language satisfy a certain condition (specified by a complementary combination of STP and MJN multimorphisms), then any instance can be solved in polynomial time (via a new algorithm developed in this article), otherwise the language is NP-hard. This is the first complete complexity classification of general-valued constraint languages over non-Boolean domains. It is a common phenomenon that complexity classifications of problems over non-Boolean domains are significantly harder than the Boolean cases. The polynomial-time algorithm we present for the tractable cases is a generalization of the submodular minimization problem and a result of Cohen et al. [2008].   Our results generalize previous results by Takhanov [2010a] and (a subset of results) by Cohen et al. [2006] and Deineko et al. [2008]. Moreover, our results do not rely on any computer-assisted search as in Deineko et al. [2008], and provide a powerful tool for proving hardness of finite-valued and general-valued languages.

Connected through hyperedge HE-1 (induced by source paper "representing fitness landscapes by valued constraints to understand the complexity of local search"):
  with members:
    - "the complexity of general valued csps seen from the other side"
    - "the complexity of finite valued csps"
    - "necessary conditions for tractability of valued csps"
    - "an algebraic theory of complexity for discrete optimization"

Connected through hyperedge HE-2 (induced by source paper "holant clones and the approximability of conservative holant problems"):
  with members:
    - "boolean approximate counting csps with weak conservativity and implications for ferromagnetic two spin"
    - "inapproximability of the independent set polynomial in the complex plane"
    - "functional clones and expressibility of partition functions"
    - "the complexity of holant problems over boolean domain with non negative weights"
    - "the complexity of approximating conservative counting csps"
    - "the expressibility of functions on the boolean domain with applications to counting csps"
    - "complexity of counting csp with complex weights"
    - "approximation complexity of complex weighted degree two counting constraint satisfaction problems"

Connected through hyperedge HE-3 (induced by source paper "on planar valued csps"):
  with members:
    - "the power of sherali adams relaxations for general valued csps"
    - "even delta matroids and the complexity of planar boolean csps"
    - "the complexity of finite valued csps"
    - "a galois connection for weighted relational clones of infinite size"
    - "necessary conditions for tractability of valued csps"
    - "hybrid v csps and algebraic reductions"
    - "algebraic properties of valued constraint satisfaction problem"
    - "the complexity of general valued csps"

Connected through hyperedge HE-4 (induced by source paper "binarisation for valued constraint satisfaction problems"):
  with members:
    - "the power of sherali adams relaxations for general valued csps"
    - "a galois connection for weighted relational clones of infinite size"
    - "a finer reduction of constraint problems to digraphs"
    - "algebraic properties of valued constraint satisfaction problem"
    - "a reduction from valued csp to min cost homomorphism problem for digraphs"
    - "the complexity of general valued csps"
    - "sherali adams relaxations for valued csps"
    - "robustly solvable constraint satisfaction problems"

Connected through hyperedge HE-5 (induced by source paper "the complexity of finite valued csps"):
  with members:
    - "an algebraic theory of complexity for discrete optimization"
    - "the power of linear programming for general valued csps"
    - "computational complexity of the minimum cost homomorphism problem on three element domains"
    - "tractable triangles and cross free convexity in discrete optimisation"
    - "the power of linear programming for valued csps"
    - "an effective fusion technique of cloud computing and networking series"
    - "tractable optimization problems through hypergraph based structural restrictions"
    - "the power of linear programming for valued csps a constructive characterization"

Connected through hyperedge HE-6 (induced by source paper "even delta matroids and the complexity of planar boolean csps"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "the complexity of finite valued csps"
    - "algebraic properties of valued constraint satisfaction problem"
    - "the complexity of general valued csps"
    - "effectiveness of structural restrictions for hybrid csps"
    - "approximate constraint satisfaction requires large lp relaxations"
    - "hybrid tractability of valued constraint problems"

Connected through hyperedge HE-7 (induced by source paper "a galois connection for weighted relational clones of infinite size"):
  with members:
    - "the power of sherali adams relaxations for general valued csps"
    - "the complexity of finite valued csps"
    - "necessary conditions for tractability of valued csps"
    - "algebraic properties of valued constraint satisfaction problem"
    - "the complexity of general valued csps"
    - "sherali adams relaxations for valued csps"
    - "an algebraic theory of complexity for discrete optimization"
    - "the power of linear programming for general valued csps"

Connected through hyperedge HE-8 (induced by source paper "backdoors to tractable valued csp"):
  with members:
    - "the complexity landscape of decompositional parameters for ilp"
    - "differential game logic"
    - "necessary conditions for tractability of valued csps"
    - "discovering archipelagos of tractability for constraint satisfaction and counting"
    - "on backdoors to tractable constraint languages"
    - "backdoors to normality for disjunctive logic programs"
    - "backdoors to satisfaction"
    - "backdoors to tractable answer set programming"

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)