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: "temporal matching"
  Abstract: A link stream is a sequence of pairs of the form $(t,\{u,v\})$, where $t\in\mathbb N$ represents a time instant and $u\neq v$. Given an integer $\gamma$, the $\gamma$-edge between vertices $u$ and $v$, starting at time $t$, is the set of temporally consecutive edges defined by $\{(t',\{u,v\}) | t' \in [t,t+\gamma-1]\}$. We introduce the notion of temporal matching of a link stream to be an independent $\gamma$-edge set belonging to the link stream. We show that the problem of computing a temporal matching of maximum size is NP-hard as soon as $\gamma>1$. We depict a kernelization algorithm parameterized by the solution size for the problem. As a byproduct we also give a $2$-approximation algorithm.  Both our $2$-approximation and kernelization algorithms are implemented and confronted to link streams collected from real world graph data. We observe that finding temporal matchings is a sensitive question when mining our data from such a perspective as: managing peer-working when any pair of peers $X$ and $Y$ are to collaborate over a period of one month, at an average rate of at least two email exchanges every week. We furthermore design a link stream generating process by mimicking the behaviour of a random moving group of particles under natural simulation, and confront our algorithms to these generated instances of link streams. All the implementations are open source.

Connected through hyperedge HE-1 (induced by source paper "multistage graph problems on a global budget"):
  with members:
    - "multistage vertex cover"
    - "computing maximum matchings in temporal graphs"
    - "online multistage subset maximization problems"
    - "multistage knapsack"
    - "changing bases multistage optimization for matroids and matchings"
    - "facility location in evolving metrics"
    - "contracting graphs to paths and trees"

Connected through hyperedge HE-2 (induced by source paper "computing maximum matchings in temporal graphs"):
  with members:
    - "how fast can we reach a target vertex in stochastic temporal graphs"
    - "fixed parameter algorithms for maximum profit facility location under matroid constraints"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "temporal vertex cover with a sliding time window"
    - "how to match when all vertices arrive online"
    - "sliding window temporal graph coloring"
    - "efficient and adaptive parameterized algorithms on modular decompositions"
    - "fully polynomial fpt algorithms for some classes of bounded clique width graphs"

Connected through hyperedge HE-3 (induced by source paper "temporal vertex cover with a sliding time window"):
  with members:
    - "how fast can we reach a target vertex in stochastic temporal graphs"
    - "the temporal explorer who returns to the base"
    - "sliding window temporal graph coloring"
    - "on problems as hard as cnf sat"
    - "adapting the bron kerbosch algorithm for enumerating maximal cliques in temporal graphs"
    - "computing maximal cliques in link streams"
    - "temporal network optimization subject to connectivity constraints"
    - "on temporal graph exploration"

Connected through hyperedge HE-4 (induced by source paper "comparing temporal graphs using dynamic time warping"):
  with members:
    - "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"
    - "multistage problems on a global budget"
    - "computing maximum matchings in temporal graphs"
    - "the computational complexity of finding temporal paths under waiting time constraints"
    - "fast exact dynamic time warping on run length encoded time series"
    - "multi net a scalable multiplex network embedding framework"
    - "alignment of dynamic networks"
    - "the complexity of finding small separators in temporal graphs"

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)