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 approximability of three valued max csp"
  Abstract: In the maximum constraint satisfaction problem (Max CSP), one is given a finite collection of (possibly weighted) constraints on overlapping sets of variables, and the goal is to assign values from a given domain to the variables so as to maximize the number (or the total weight, for the weighted case) of satisfied constraints. This problem is NP-hard in general, and, therefore, it is natural to study how restricting the allowed types of constraints affects the approximability of the problem. It is known that every Boolean (that is, two-valued) Max CSP problem with a finite set of allowed constraint types is either solvable exactly in polynomial time or else APX-complete (and hence can have no polynomial time approximation scheme unless P=NP. It has been an open problem for several years whether this result can be extended to non-Boolean Max CSP, which is much more difficult to analyze than the Boolean case. In this paper, we make the first step in this direction by establishing this result for Max CSP over a three-element domain. Moreover, we present a simple description of all polynomial-time solvable cases of our problem. This description uses the well-known algebraic combinatorial property of supermodularity. We also show that every hard three-valued Max CSP problem contains, in a certain specified sense, one of the two basic hard Max CSP problems which are the Maximum k-colourable subgraph problems for k=2,3.

Connected through hyperedge HE-1 (induced by source paper "the complexity of boolean surjective general valued csps"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "the complexity of finite valued csps"
    - "a galois connection for weighted relational clones of infinite size"
    - "algebraic properties of valued constraint satisfaction problem"
    - "the complexity of general valued csps"
    - "an algebraic hardness criterion for surjective constraint satisfaction"
    - "an algebraic theory of complexity for discrete optimization"

Connected through hyperedge HE-2 (induced by source paper "the complexity of finite valued csps"):
  with members:
    - "an algebraic theory of complexity for discrete optimization"
    - "the complexity of conservative valued csps"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the complexity of general valued csps"):
  with members:
    - "the power of sherali adams relaxations for general valued csps"
    - "combinatorial optimization algorithms via polymorphisms"
    - "a galois connection for valued constraint languages of infinite size"
    - "algebraic properties of valued constraint satisfaction problem"
    - "effectiveness of structural restrictions for hybrid csps"
    - "local distribution and the symmetry gap approximability of multiway partitioning problems"
    - "sherali adams relaxations for valued csps"
    - "robustly solvable constraint satisfaction problems"

Connected through hyperedge HE-4 (induced by source paper "an algebraic theory of complexity for discrete optimization"):
  with members:
    - "the complexity of conservative valued csps"
    - "the power of linear programming for valued csps"
    - "hybrid tractability of valued constraint problems"
    - "the expressive power of binary submodular functions"
    - "the approximability of max csp with fixed value constraints"

Connected through hyperedge HE-5 (induced by source paper "the complexity of surjective homomorphism problems a survey"):
  with members:
    - "finding vertex surjective graph homomorphisms"
    - "approximation for maximum surjective constraint satisfaction problems"
    - "the complexity of rooted phylogeny problems"
    - "the complexity of global cardinality constraints"
    - "the stubborn problem is stubborn no more a polynomial algorithm for 3 compatible colouring and the stubborn list partition problem"
    - "orthomodular lattices foulis semigroups and dagger kernel categories"

Connected through hyperedge HE-6 (induced by source paper "min csp on four elements moving beyond submodularity"):
  with members:
    - "a dichotomy theorem for conservative general valued csps"
    - "the complexity of conservative finite valued csps"
    - "the approximability of max csp with fixed value constraints"

Connected through hyperedge HE-7 (induced by source paper "the expressive power of binary submodular functions"):
  with members:
    - "a maximal tractable class of soft constraints"
    - "the approximability of max csp with fixed value constraints"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of submodular function minimisation on diamonds"):
  with members:
    - "the approximability of max csp with fixed value constraints"

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)