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: "diminishable parameterized problems and strict polynomial kernelization"
  Abstract: Kernelization -- the mathematical key concept for provably effective polynomial-time preprocessing of NP-hard problems -- plays a central role in parameterized complexity and has triggered an extensive line of research. This is in part due to a lower bounds framework that allows to exclude polynomial-size kernels under the assumption that NP is not a subset of coNP/poly. In this paper we consider a stronger yet very natural variant of kernelization, namely strict kernelization, where one is not allowed to increase the parameter of the reduced instance (the kernel) by more than an additive constant. Building on earlier work of Chen, Flum, and M\"uller [Theory Comput. Syst. 2011] and developing a general framework, we show that a wide variety of FPT problems does not admit strict polynomial kernels under the much weaker assumption that P is not equal to NP. In particular, we show that various (multicolored) graph problems, set covering problems, and Turing machine computation problems do not admit strict polynomial kernels unless P = NP. To this end, a key concept we use are diminishable problems; these are parameterized problems that allow to decrease the parameter of the input instance by at least one in polynomial time, thereby outputting an equivalent problem instance. Finally, we find that relaxing the concept of strict kernels to kernels with a constant-factor increase of the parameter leads to a scenario in which we can show for a number of problems that the framework is not applicable assuming that the (Strong) Exponential Time Hypothesis holds.

Connected through hyperedge HE-1 (induced by source paper "kernelization lower bounds for finding constant size subgraphs"):
  with members:
    - "when can graph hyperbolicity be computed in linear time"
    - "the power of data reduction for matching"
    - "linear time kernelization for feedback vertex set"
    - "fully polynomial time parameterized computations for graphs and matrices of low treewidth"
    - "polynomial fixed parameter algorithms a case study for longest path on interval graphs"
    - "why walking the dog takes time frechet distance has no strongly subquadratic algorithms unless seth fails"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "kernelization lower bounds by cross composition"

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)