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: "a note on max k vertex cover faster fpt as smaller approximate kernel and improved approximation"
  Abstract: In Maximum $k$-Vertex Cover (Max $k$-VC), the input is an edge-weighted graph $G$ and an integer $k$, and the goal is to find a subset $S$ of $k$ vertices that maximizes the total weight of edges covered by $S$. Here we say that an edge is covered by $S$ iff at least one of its endpoints lies in $S$. #R##N#We present an FPT approximation scheme (FPT-AS) that runs in $(1/\epsilon)^{O(k)} poly(n)$ time for the problem, which improves upon Gupta et al.'s $(k/\epsilon)^{O(k)} poly(n)$-time FPT-AS [SODA'18, FOCS'18]. Our algorithm is simple: just use brute force to find the best $k$-vertex subset among the $O(k/\epsilon)$ vertices with maximum weighted degrees. #R##N#Our algorithm naturally yields an efficient approximate kernelization scheme of $O(k/\epsilon)$ vertices; previously, an $O(k^5/\epsilon^2)$-vertex approximate kernel is only known for the unweighted version of Max $k$-VC [Lokshtanov et al., STOC'17]. Interestingly, this has an application outside of parameterized complexity: using our approximate kernelization as a preprocessing step, we can directly apply Raghavendra and Tan's SDP-based algorithm for 2SAT with cardinality constraint [SODA'12] to give an $0.92$-approximation for Max $k$-VC in polynomial time. This improves upon Feige and Langberg's algorithm [J. Algorithms'01] which yields $(0.75 + \delta)$-approximation for some (unspecified) constant $\delta > 0$. #R##N#We also consider the minimization version (Min $k$-VC), where the goal is to minimize the total weight of edges covered by $S$. We provide an FPT-AS for Min $k$-VC with similar running time of $(1/\epsilon)^{O(k)} poly(n)$, which again improves on a $(k/\epsilon)^{O(k)} poly(n)$-time FPT-AS of Gupta et al. On the other hand, we show that there is unlikely a polynomial size approximate kernelization for Min $k$-VC for any factor less than two.

Connected through hyperedge HE-1 (induced by source paper "global cardinality constraints make approximating some max 2 csps harder"):
  with members:
    - "resolution lower bounds for refutation statements"
    - "a dichotomy theorem for nonuniform csps"
    - "the proof of csp dichotomy conjecture"
    - "analysis of boolean functions"
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "approximating csps with global cardinality constraints using sdp hierarchies"
    - "the complexity of global cardinality constraints"
    - "conditional hardness for approximate coloring"

Connected through hyperedge HE-2 (induced by source paper "a polynomial time approximation schema for maximum k vertex cover in bipartite graphs"):
  with members:
    - "approximating low dimensional coverage problems"

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.DS(Data Structures and Algorithms)