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: "weighted matching in the semi streaming model"
  Abstract: We reduce the best known approximation ratio for finding a weighted matching of a graph using a one-pass semi-streaming algorithm from 5.828 to 5.585. The semi-streaming model forbids random access to the input and restricts the memory to O(n*polylog(n)) bits. It was introduced by Muthukrishnan in 2003 and is appropriate when dealing with massive graphs.

Connected through hyperedge HE-1 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams"):
  with members:
    - "approximate maximum matching in random streams"
    - "polynomial pass lower bounds for graph streaming algorithms"
    - "dynamic streaming spectral sparsification in nearly linear time and space"
    - "weighted matchings via unweighted augmentations"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "tight space approximation tradeoff for the multi pass streaming set cover problem"
    - "simplified and space optimal semi streaming for 2 epsilon approximate matching"
    - "on estimating maximum matching size in graph streams"

Connected through hyperedge HE-2 (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-3 (induced by source paper "approximate maximum matching in random streams"):
  with members:
    - "weighted matchings via unweighted augmentations"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "maximum matching in two three and a few more passes over graph streams"
    - "simplified and space optimal semi streaming for 2 epsilon approximate matching"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "fully dynamic matching in bipartite graphs"
    - "better bounds for matchings in the streaming model"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-4 (induced by source paper "on estimating maximum matching size in graph streams"):
  with members:
    - "on approximating functions of the singular values in a stream"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "on the communication complexity of linear algebraic problems in the message passing model"
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "better bounds for matchings in the streaming model"
    - "superlinear lower bounds for multipass graph processing"
    - "maximum matching in semi streaming with few passes"

Connected through hyperedge HE-5 (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"
    - "laminar families and metric embeddings non bipartite maximum matching problem in the semi streaming model"
    - "maximum matching in semi streaming with few passes"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-6 (induced by source paper "maximum matching in two three and a few more passes over graph streams"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "a 2 ϵ approximation for maximum weight matching in the semi streaming model"
    - "streaming algorithms for submodular function maximization"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "better bounds for matchings in the streaming model"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a 2 ϵ approximation for maximum weight matching in the semi streaming model"):
  with members:
    - "on estimating maximum matching size in graph streams"
    - "streaming weighted matchings optimal meets greedy"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "maximum matching in turnstile streams"
    - "submodular maximization meets streaming matchings matroids and more"
    - "better bounds for matchings in the streaming model"
    - "improved approximation guarantees for weighted matching in the semi streaming model"
    - "buyback problem approximate matroid intersection with cancellation costs"

Connected through hyperedge HE-8 (induced by source paper "estimating weighted matchings in o n space"):
  with members:
    - "the shortest augmenting paths for online matchings on trees"
    - "streaming weighted matchings optimal meets greedy"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "better bounds for matchings in the streaming model"
    - "simple deterministic algorithms for fully dynamic maximal matching"
    - "maintaining approximate maximum weighted matching in fully dynamic graphs"
    - "maximum matching in semi streaming with few passes"
    - "fully dynamic maximal matching in o log n update time"

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.DM(Discrete Mathematics)