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: "computing hitting set kernels by ac 0 circuits"
  Abstract: Given a hypergraph $H = (V,E)$, what is the smallest subset $X \subseteq V$ such that $e \cap X \neq \emptyset$ holds for all $e \in E$? This problem, known as the hitting set problem, is a basic problem in parameterized complexity theory. There are well-known kernelization algorithms for it, which get a hypergraph $H$ and a number $k$ as input and output a hypergraph $H'$ such that (1) $H$ has a hitting set of size $k$ if, and only if, $H'$ has such a hitting set and (2) the size of $H'$ depends only on $k$ and on the maximum cardinality $d$ of edges in $H$. The algorithms run in polynomial time, but are highly sequential. Recently, it has been shown that one of them can be parallelized to a certain degree: one can compute hitting set kernels in parallel time $O(d)$ -- but it was conjectured that this is the best parallel algorithm possible. We refute this conjecture and show how hitting set kernels can be computed in constant parallel time. For our proof, we introduce a new, generalized notion of hypergraph sunflowers and show how iterated applications of the color coding technique can sometimes be collapsed into a single application.

Connected through hyperedge HE-1 (induced by source paper "optimal size problem kernels for d hitting set in linear time and space"):
  with members:
    - "understanding the effectiveness of data reduction in public transportation networks"
    - "on approximate data reduction for the rural postman problem theory and experiments"
    - "efficiently enumerating hitting sets of hypergraphs arising in data profiling"
    - "exploiting hidden structure in selecting dimensions that distinguish vectors"
    - "a shortcut to sun flowers kernels in logarithmic space or linear time"
    - "towards optimal and expressive kernelization for d hitting set"

Connected through hyperedge HE-2 (induced by source paper "on the descriptive complexity of color coding"):
  with members:
    - "slicewise definability in first order logic with bounded quantifier rank"
    - "on the parallel parameterized complexity of the graph isomorphism problem"
    - "fast parallel fixed parameter algorithms via color coding"

Connected through hyperedge HE-3 (induced by source paper "towards work efficient parallel parameterized algorithms"):
  with members:
    - "computing kernels in parallel lower and upper bounds"
    - "fast parallel fixed parameter algorithms via color coding"

Connected through hyperedge HE-4 (induced by source paper "the parameterized space complexity of model checking bounded variable first order logic"):
  with members:
    - "fast parallel fixed parameter algorithms via color coding"
    - "one hierarchy spawns another graph deconstructions and the complexity classification of conjunctive queries"
    - "deciding first order properties of nowhere dense graphs"
    - "on the complexity of existential positive queries"
    - "bounds on monotone switching networks for directed connectivity"
    - "fixed parameter tractability definability and model checking"

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)