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: "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
  Abstract: In this paper, we study the non-bipartite maximum matching problem in the semi-streaming model. The maximum matching problem in the semi-streaming model has received a significant amount of attention lately. While the problem has been somewhat well solved for bipartite graphs, the known algorithms for non-bipartite graphs use $2^{\frac1\epsilon}$ passes or $n^{\frac1\epsilon}$ time to compute a $(1-\epsilon)$ approximation. In this paper we provide the first FPTAS (polynomial in $n,\frac1\epsilon$) for the problem which is efficient in both the running time and the number of passes. We also show that we can estimate the size of the matching in $O(\frac1\epsilon)$ passes using slightly superlinear space. #R##N#To achieve both results, we use the structural properties of the matching polytope such as the laminarity of the tight sets and total dual integrality. The algorithms are iterative, and are based on the fractional packing and covering framework. However the formulations herein require exponentially many variables or constraints. We use laminarity, metric embeddings and graph sparsification to reduce the space required by the algorithms in between and across the iterations. This is the first use of these ideas in the semi-streaming model to solve a combinatorial optimization problem.

Connected through hyperedge HE-1 (induced by source paper "space efficient approximation to maximum matching size from uniform edge samples"):
  with members:
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "extractor based time space lower bounds for learning"
    - "estimating graph parameters from random order streams"
    - "testable bounded degree graph properties are random order streamable"
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"

Connected through hyperedge HE-2 (induced by source paper "communication complexity of approximate maximum matching in the message passing model"):
  with members:
    - "welfare maximization with limited interaction"
    - "maximum matching in turnstile streams"
    - "economic efficiency requires interaction"
    - "when distributed computation is communication expensive"
    - "better bounds for matchings in the streaming model"
    - "maximum matching in semi streaming with few passes"
    - "sorting searching and simulation in the mapreduce framework"
    - "improved approximation guarantees for weighted matching in the semi streaming model"

Connected through hyperedge HE-3 (induced by source paper "almost optimal streaming algorithms for coverage problems"):
  with members:
    - "submodular optimization under noise"
    - "distributed coverage maximization via sketching"
    - "better streaming algorithms for the maximum coverage problem"
    - "tight bounds for single pass streaming complexity of the set cover problem"
    - "towards tight bounds for the streaming set cover problem"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "randomized composable core sets for distributed submodular maximization"

Connected through hyperedge HE-4 (induced by source paper "kernelization via sampling with applications to dynamic graph streams"):
  with members:
    - "correlation clustering in data streams"
    - "densest subgraph in dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "applications of uniform sampling densest subgraph and beyond"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "sketching cuts in graphs and hypergraphs"

Connected through hyperedge HE-5 (induced by source paper "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"):
  with members:
    - "near linear time approximation schemes for uncapacitated and capacitated b matching problems in nonbipartite graphs"
    - "a local computation approximation scheme to maximum matching"
    - "better bounds for matchings in the streaming model"
    - "lower bounds for adaptive sparse recovery"
    - "superlinear lower bounds for multipass graph processing"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "weighted matching in the semi streaming model"

Connected through hyperedge HE-6 (induced by source paper "near linear time approximation schemes for uncapacitated and capacitated b matching problems in nonbipartite graphs"):
  with members:
    - "faster approximate multicommodity flow using quadratically coupled flows"
    - "scaling algorithms for approximate and exact maximum weight matching"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-7 (induced by source paper "linear programming in the semi streaming model with application to the maximum matching problem"):
  with members:
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "graph sparsification in the semi streaming model"
    - "weighted matching in the semi streaming model"
    - "randomized rounding without solving the linear program"

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)