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: "the inapproximability of maximum single sink unsplittable priority and confluent flow problems"
  Abstract: We consider single-sink network flow problems. An instance consists of a capacitated graph (directed or undirected), a sink node $t$ and a set of demands that we want to send to the sink. Here demand $i$ is located at a node $s_i$ and requests an amount $d_i$ of flow capacity in order to route successfully. Two standard objectives are to maximise (i) the number of demands (cardinality) and (ii) the total demand (throughput) that can be routed subject to the capacity constraints. Furthermore, we examine these maximisation problems for three specialised types of network flow: unsplittable, confluent and priority flows. #R##N#In the {\em unsplittable flow} problem (UFP), we have edge capacities, and the demand for $s_i$ must be routed on a single path. In the {\em confluent flow} problem, we have node capacities, and the final flow must induce a tree. Both of these problems have been studied extensively, primarily in the single-sink setting. However, most of this work imposed the {\em no-bottleneck assumption} (that the maximum demand $d_{max}$ is at most the minimum capacity $u_{min}$). Given the no-bottleneck assumption (NBA), there is a factor $4.43$-approximation algorithm due to Dinitz et al. for the unsplittable flow problem. Under the stronger assumption of uniform capacities, there is a factor $3$-approximation algorithm due to Chen et al. for the confluent flow problem. However, unlike the UFP, we show that a constant factor approximation algorithm cannot be obtained for the single-sink confluent flows even {\bf with} the NBA. #R##N#Without NBA, we show that maximum cardinality single-sink UFP is hard to approximate to within a factor $n^{.5-\epsilon}$ even when all demands lie in a small interval $[1,1+\Delta]$ where $\Delta>0$ (but has polynomial input size). This is very sharp since when $\Delta=0$, this becomes a maximum flow problem.

Connected through hyperedge HE-1 (induced by source paper "minmax regret for sink location on paths with general capacities"):
  with members:
    - "minmax centered k partitioning of trees and applications to sink evacuation with dynamic confluent flows"
    - "minmax regret k sink location on a dynamic tree network with uniform capacities"

Connected through hyperedge HE-2 (induced by source paper "minmax regret k sink location on a dynamic tree network with uniform capacities"):
  with members:
    - "minmax centered k partitioning of trees and applications to sink evacuation with dynamic confluent flows"
    - "non approximability and polylogarithmic approximations of the single sink unsplittable and confluent dynamic flow problems"

Connected through hyperedge HE-3 (induced by source paper "minmax centered k partitioning of trees and applications to sink evacuation with dynamic confluent flows"):
  with members:
    - "non approximability and polylogarithmic approximations of the single sink unsplittable and confluent dynamic flow problems"

Connected through hyperedge HE-4 (induced by source paper "optimal evacuation flows on dynamic paths with general edge capacities"):
  with members:
    - "improved algorithms for multiple sink location problems in dynamic path networks"

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)