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: "perfect matchings in o n 1 5 time in regular bipartite graphs"
  Abstract: We consider the well-studied problem of finding a perfect matching in $d$-regular bipartite graphs with $2n$ vertices and $m = nd$ edges. While the best-known algorithm for general bipartite graphs (due to Hopcroft and Karp) takes $O(m \sqrt{n})$ time, in regular bipartite graphs, a perfect matching is known to be computable in $O(m)$ time. Very recently, the $O(m)$ bound was improved to $O(\min\{m, \frac{n^{2.5}\ln n}{d}\})$ expected time, an expression that is bounded by $\tilde{O}(n^{1.75})$. In this paper, we further improve this result by giving an $O(\min\{m, \frac{n^2\ln^3 n}{d}\})$ expected time algorithm for finding a perfect matching in regular bipartite graphs; as a function of $n$ alone, the algorithm takes expected time $O((n\ln n)^{1.5})$. #R##N#To obtain this result, we design and analyze a two-stage sampling scheme that reduces the problem of finding a perfect matching in a regular bipartite graph to the same problem on a subsampled bipartite graph with $O(n\ln n)$ edges that has a perfect matching with high probability. The matching is then recovered using the Hopcroft-Karp algorithm. While the standard analysis of Hopcroft-Karp gives us an $\tilde{O}(n^{1.5})$ running time, we present a tighter analysis for our special case that results in the stronger $\tilde{O}(\min\{m, \frac{n^2}{d} \})$ time mentioned earlier. #R##N#Our proof of correctness of this sampling scheme uses a new correspondence theorem between cuts and Hall's theorem ``witnesses'' for a perfect matching in a bipartite graph that we prove. We believe this theorem may be of independent interest; as another example application, we show that a perfect matching in the support of an $n \times n$ doubly stochastic matrix with $m$ non-zero entries can be found in expected time $\tilde{O}(m + n^{1.5})$.

Connected through hyperedge HE-1 (induced by source paper "subexponential and linear subpacketization coded caching via line graphs and projective geometry"):
  with members:
    - "some new constructions of coded caching schemes with reduced subpacketization"
    - "a multi antenna coded caching scheme with linear subpacketization"
    - "decentralized coded caching without file splitting"
    - "subpacketization rate trade off in multi antenna coded caching"
    - "coded caching based on combinatorial designs"
    - "a unified framework for constructing centralized coded caching schemes"
    - "low subpacketization coded caching via projective geometry for broadcast and d2d networks"
    - "coded caching via projective geometry a new low subpacketization scheme"

Connected through hyperedge HE-2 (induced by source paper "fair by design matching"):
  with members:
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "balancing lexicographic fairness and a utilitarian objective with application to kidney exchange"
    - "algorithmic decision making and the cost of fairness"
    - "fairness in learning classic and contextual bandits"
    - "nash social welfare approximation for strategic agents"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "spectral analysis of matrix scaling and operator scaling"):
  with members:
    - "operator scaling via geodesically convex optimization invariant theory and polynomial identity testing"
    - "testing halfspaces over rotation invariant distributions"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "algorithmic and optimization aspects of brascamp lieb inequalities via operator scaling"
    - "operator scaling theory and applications"
    - "a deterministic polynomial time algorithm for non commutative rational identity testing"
    - "algorithms and hardness for robust subspace recovery"

Connected through hyperedge HE-4 (induced by source paper "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"):
  with members:
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "densest subgraph in dynamic graph streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "single pass spectral sparsification in dynamic streams"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "densest subgraph in streaming and mapreduce"
    - "tight bounds for lp samplers finding duplicates in streams and related problems"

Connected through hyperedge HE-5 (induced by source paper "navigating central path with electrical flows from flows to matchings and back"):
  with members:
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "nearly maximum flows in nearly linear time"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "approaching optimality for solving sdd systems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "faster approximate lossy generalized flow via interior point algorithms"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-6 (induced by source paper "maximum matchings via glauber dynamics"):
  with members:
    - "improved inapproximability results for counting independent sets in the hard core model"
    - "computational transition at the uniqueness threshold"
    - "perfect matchings via uniform sampling in regular bipartite 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)