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 "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.

Includes members:
  Member "on the hardness of losing weight":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the complexity of restoring corrupted colorings")
  Member "a dichotomy theorem for nonuniform csps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when symmetries are not enough a hierarchy of hard constraint satisfaction problems")
  Member "the proof of csp dichotomy conjecture":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal polynomial time compression for boolean max csp")
  Member "optimal sparsification for some binary csps using low degree polynomials":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized complexity of conflict free graph coloring")
  Member "sparsification upper and lower bounds for graph problems and not all equal sat":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal polynomial time compression for boolean max csp")
  Member "an effective dichotomy for the counting constraint satisfaction problem":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximate counting csp seen from the other side")
  Member "preprocessing of min ones problems a dichotomy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal polynomial time compression for boolean max csp")

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)