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: "derandomized concentration bounds for polynomials and hypergraph maximal independent set"
  Abstract: A parallel algorithm for maximal independent set (MIS) in hypergraphs has been a long-standing algorithmic challenge, dating back nearly 30 years to a survey of Karp & Ramachandran (1990). The best randomized parallel algorithm for hypergraphs of fixed rank $r$ was developed by Beame & Luby (1990) and Kelsen (1992), running in time roughly $(\log n)^{r!}$. #R##N#We improve the randomized algorithm of Kelsen, reducing the runtime to roughly $(\log n)^{2^r}$ and simplifying the analysis through the use of more-modern concentration inequalities. We also give a method for derandomizing concentration bounds for low-degree polynomials, which are the key technical tool used to analyze that algorithm. This leads to a deterministic PRAM algorithm also running in $(\log n)^{2^{r+3}}$ time and $\text{poly}(m,n)$ processors. This is the first deterministic algorithm with sub-polynomial runtime for hypergraphs of rank $r > 3$. #R##N#Our analysis can also apply when $r$ is slowly growing; using this in conjunction with a strategy of Bercea et al. (2015) gives a deterministic MIS algorithm running in time $\exp(O( \frac{\log (mn)}{\log \log (mn)}))$.

Connected through hyperedge HE-1 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space"):
  with members:
    - "walking randomly massively and efficiently"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "deterministic distributed dominating set approximation in the congest model"
    - "efficient deterministic distributed coloring with small bandwidth"
    - "exponentially faster massively parallel maximal matching"
    - "congested clique algorithms for graph spanners"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-2 (induced by source paper "distributed approximation algorithms for maximum matching in graphs and hypergraphs"):
  with members:
    - "distributed dense subgraph detection and low outdegree orientation"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "lower bounds for maximal matchings and maximal independent sets"
    - "parameterized distributed algorithms"
    - "improved deterministic distributed matching via rounding"
    - "deterministic distributed edge coloring with fewer colors"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "adapting sequential algorithms to the distributed setting"

Connected through hyperedge HE-3 (induced by source paper "efficient distributed computation of mis and generalized mis in linear hypergraphs"):
  with members:
    - "derandomizing local distributed algorithms under bandwidth restrictions"
    - "on the complexity of local distributed graph problems"
    - "distributed strong diameter network decomposition"
    - "an improved distributed algorithm for maximal independent set"
    - "distributed symmetry breaking in hypergraphs"
    - "on computing maximal independent sets of hypergraphs in parallel"
    - "the locality of distributed symmetry breaking"

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)