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: "an fpt algorithm beating 2 approximation for k cut"
  Abstract: In the $k$-Cut problem, we are given an edge-weighted graph $G$ and an integer $k$, and have to remove a set of edges with minimum total weight so that $G$ has at least $k$ connected components. Prior work on this problem gives, for all $h \in [2,k]$, a $(2-h/k)$-approximation algorithm for $k$-cut that runs in time $n^{O(h)}$. Hence to get a $(2 - \varepsilon)$-approximation algorithm for some absolute constant $\varepsilon$, the best runtime using prior techniques is $n^{O(k\varepsilon)}$. Moreover, it was recently shown that getting a $(2 - \varepsilon)$-approximation for general $k$ is NP-hard, assuming the Small Set Expansion Hypothesis. #R##N#If we use the size of the cut as the parameter, an FPT algorithm to find the exact $k$-Cut is known, but solving the $k$-Cut problem exactly is $W[1]$-hard if we parameterize only by the natural parameter of $k$. An immediate question is: \emph{can we approximate $k$-Cut better in FPT-time, using $k$ as the parameter?} #R##N#We answer this question positively. We show that for some absolute constant $\varepsilon > 0$, there exists a $(2 - \varepsilon)$-approximation algorithm that runs in time $2^{O(k^6)} \cdot \widetilde{O} (n^4)$. This is the first FPT algorithm that is parameterized only by $k$ and strictly improves the $2$-approximation.

Connected through hyperedge HE-1 (induced by source paper "the number of minimum k cuts improving the karger stein bound"):
  with members:
    - "lp relaxation and tree packing for minimum k cuts"
    - "fast and deterministic approximations for k cut"
    - "faster exact and approximate algorithms for k cut"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "minimum k way cut of bounded size is fixed parameter tractable"
    - "tight approximation ratio of a general greedy splitting algorithm for the minimum k way cut problem"

Connected through hyperedge HE-2 (induced by source paper "fast and deterministic approximations for k cut"):
  with members:
    - "lp relaxation and tree packing for minimum k cuts"
    - "on approximating sparse covering integer programs"
    - "faster exact and approximate algorithms for k cut"
    - "fast approximations for metric tsp via linear programming"
    - "approximating the held karp bound for metric tsp in nearly linear time"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "nearly linear time approximation schemes for mixed packing covering and facility location linear programs"
    - "designing fpt algorithms for cut problems using randomized contractions"

Connected through hyperedge HE-3 (induced by source paper "faster exact and approximate algorithms for k cut"):
  with members:
    - "powers of tensors and fast matrix multiplication"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "tight approximation ratio of a general greedy splitting algorithm for the minimum k way cut problem"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-4 (induced by source paper "a note on max k vertex cover faster fpt as smaller approximate kernel and improved approximation"):
  with members:
    - "better balance by being biased a 0 8776 approximation for max bisection"
    - "reductions between expansion 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)