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: "kernelization of constraint satisfaction problems a study through universal algebra"
  Abstract: A kernelization algorithm for a computational problem is a procedure which compresses an instance into an equivalent instance whose size is bounded with respect to a complexity parameter. For the Boolean satisfiability problem (SAT), and the constraint satisfaction problem (CSP), there exist many results concerning upper and lower bounds for kernelizability of specific problems, but it is safe to say that we lack general methods to determine whether a given SAT problem admits a kernel of a particular size. This could be contrasted to the currently flourishing research program of determining the classical complexity of finite-domain CSP problems, where almost all non-trivial tractable classes have been identified with the help of algebraic properties. In this paper, we take an algebraic approach to the problem of characterizing the kernelization limits of NP-hard SAT and CSP problems, parameterized by the number of variables. Our main focus is on problems admitting linear kernels, as has, somewhat surprisingly, previously been shown to exist. We show that a CSP problem has a kernel with O(n) constraints if it can be embedded (via a domain extension) into a CSP problem which is preserved by a Maltsev operation. We also study extensions of this towards SAT and CSP problems with kernels with O(n^c) constraints, c>1, based on embeddings into CSP problems preserved by a k-edge operation, k > c. These results follow via a variant of the celebrated few subpowers algorithm. In the complementary direction, we give indication that the Maltsev condition might be a complete characterization of SAT problems with linear kernels, by showing that an algebraic condition that is shared by all problems with a Maltsev embedding is also necessary for the existence of a linear kernel unless NP is included in co-NP/poly.

Connected through hyperedge HE-1 (induced by source paper "optimal polynomial time compression for boolean max csp"):
  with members:
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "tight hardness for shortest cycles and paths in sparse graphs"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "complexity of counting csp with complex weights"
    - "preprocessing of min ones problems a dichotomy"
    - "the approximability of max csp with fixed value constraints"

Connected through hyperedge HE-2 (induced by source paper "which np hard sat and csp problems admit exponentially improved algorithms"):
  with members:
    - "the proof of csp dichotomy conjecture"
    - "tight hardness for shortest cycles and paths in sparse graphs"
    - "time complexity of constraint satisfaction via universal algebra"
    - "on problems as hard as cnf sat"
    - "randomised enumeration of small witnesses using a decision oracle"
    - "exact algorithms via monotone local search"
    - "powers of tensors and fast matrix multiplication"
    - "analysis of boolean functions"

Connected through hyperedge HE-3 (induced by source paper "best case and worst case sparsifiability of boolean csps"):
  with members:
    - "optimal data reduction for graph coloring using low degree polynomials"
    - "which np hard sat and csp problems admit exponentially improved algorithms"
    - "optimal sparsification for some binary csps using low degree polynomials"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "point line cover the easy kernel is essentially tight"
    - "on sparsification for computing treewidth"
    - "weak bases of boolean co clones"
    - "kernelization lower bounds by cross composition"

Connected through hyperedge HE-4 (induced by source paper "optimal sparsification for some binary csps using low degree polynomials"):
  with members:
    - "optimal data reduction for graph coloring using low degree polynomials"
    - "best case and worst case sparsifiability of boolean csps"
    - "complexity and approximability of parameterized max csps"
    - "sparsification upper and lower bounds for graph problems and not all equal sat"
    - "nonclassical polynomials as a barrier to polynomial lower bounds"
    - "on sparsification for computing treewidth"
    - "kernelization lower bounds by cross composition"
    - "constraint satisfaction parameterized by solution size"

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)