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: "complexity and approximability of parameterized max csps"
  Abstract: We study the optimization version of constraint satisfaction problems (Max-CSPs) in the framework of parameterized complexity; the goal is to compute the maximum fraction of constraints that can be satisfied simultaneously. In standard CSPs, we want to decide whether this fraction equals one. The parameters we investigate are structural measures, such as the treewidth or the clique-width of the variable–constraint incidence graph of the CSP instance. We consider Max-CSPs with the constraint types \({\text {AND}}\), \({\text {OR}}\), \({\text {PARITY}}\), and \({\text {MAJORITY}}\), and with various parameters k, and we attempt to fully classify them into the following three cases:#R##N##R##N#1.#R##N##R##N#The exact optimum can be computed in \(\textsf {FPT}\) time.#R##N##R##N##R##N##R##N##R##N#2.#R##N##R##N#It is Open image in new window -hard to compute the exact optimum, but there is a randomized \(\textsf {FPT}\) approximation scheme (\(\textsf {FPT\text {-}AS}\)), which computes a \((1{-}\epsilon )\)-approximation in time \(f(k,\epsilon ) \cdot {\text {poly}}(n)\).#R##N##R##N##R##N##R##N##R##N#3.#R##N##R##N#There is no \(\textsf {FPT\text {-}AS}\) unless Open image in new window .#R##N##R##N##R##N##R##N##R##N##R##N##R##N#For the corresponding standard CSPs, we establish \(\textsf {FPT}\) versus Open image in new window -hardness results.

Connected through hyperedge HE-1 (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"
    - "kernelization of constraint satisfaction problems a study through universal algebra"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "parameterized compilation lower bounds for restricted cnf formulas"):
  with members:
    - "understanding model counting for beta acyclic cnf formulas"
    - "no small nondeterministic read once branching programs for cnfs of bounded treewidth"
    - "between treewidth and clique width"
    - "on obdds for cnfs of bounded treewidth"
    - "satisfiability of acyclic and almost acyclic cnf formulas"
    - "a knowledge compilation map"

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)