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: "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
  Abstract: Randomized composable coresets were introduced recently as an effective technique for solving matching and vertex cover problems in various models of computation. In this technique, one partitions the edges of an input graph randomly into multiple pieces, compresses each piece into a smaller subgraph, namely a coreset, and solves the problem on the union of these coresets to find the solution. By designing small size randomized composable coresets, one can obtain efficient algorithms, in a black-box way, in multiple computational models including streaming, distributed communication, and the massively parallel computation (MPC) model. #R##N#We develop randomized composable coresets of size $\widetilde{O}(n)$ that for any constant $\varepsilon > 0$, give a $(3/2+\varepsilon)$-approximation to matching and a $(3+\varepsilon)$-approximation to vertex cover. Our coresets improve upon the previously best approximation ratio of $O(1)$ for matching and $O(\log{n})$ for vertex cover. Most notably, our result for matching goes beyond a 2-approximation, which is a natural barrier for maximum matching in many models of computation. #R##N#Furthermore, inspired by the recent work of Czumaj et.al. (arXiv 2017), we study algorithms for matching and vertex cover in the MPC model with only $\widetilde{O}(n)$ memory per machine. Building on our coreset constructions, we develop parallel algorithms that give a $(1+\varepsilon)$-approximation to matching and $O(1)$-approximation to vertex cover in only $O_{\varepsilon}(\log\log{n})$ MPC rounds and $\widetilde{O}(n)$ memory per machine. #R##N#A key technical ingredient of our paper is a novel application of edge degree constrained subgraphs (EDCS). At the heart of our proofs are new structural properties of EDCS that identify these subgraphs as sparse certificates for large matchings and small vertex covers which are quite robust to sampling and composition.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "parallel algorithms for small subgraph counting"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "linear time subgraph counting graph degeneracy and the chasm at size six"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-3 (induced by source paper "massively parallel algorithms for distance approximation and spanners"):
  with members:
    - "walking randomly massively and efficiently"
    - "dynamic algorithms for the massively parallel computation model"
    - "mapreduce meets fine grained complexity mapreduce algorithms for apsp matrix multiplication 3 sum and beyond"
    - "polylogarithmic time deterministic network decomposition and distributed derandomization"
    - "parallel approximate undirected shortest paths via low hop emulators"
    - "faster parallel algorithm for approximate shortest path"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"

Connected through hyperedge HE-4 (induced by source paper "new lower bounds for massively parallel computation from query complexity"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "exponentially faster massively parallel maximal matching"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-5 (induced by source paper "distributed maximum matching verification in congest"):
  with members:
    - "lower bounds for maximal matchings and maximal independent sets"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "improved deterministic distributed matching via rounding"
    - "round compression for parallel matching algorithms"
    - "deterministic distributed edge coloring via hypergraph maximal matching"
    - "quadratic and near quadratic lower bounds for the congest model"
    - "on derandomizing local distributed algorithms"
    - "distributed approximation of maximum independent set and maximum matching"

Connected through hyperedge HE-6 (induced by source paper "stochastic matching with few queries 1 varepsilon approximation"):
  with members:
    - "stochastic monotone submodular maximization with queries"
    - "towards a unified theory of sparsification for matching problems"
    - "stochastic matching with few queries new algorithms and tools"
    - "the stochastic matching problem beating half with a non adaptive algorithm"
    - "almost optimal stochastic weighted matching with few queries"
    - "opting into optimal matchings"
    - "ignorance is almost bliss near optimal stochastic matching with few queries"
    - "better bounds for matchings in the streaming model"

Connected through hyperedge HE-7 (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"
    - "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"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"

Connected through hyperedge HE-8 (induced by source paper "walking randomly massively and efficiently"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "simulating random walks on graphs in the streaming model"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "testing graph clusterability algorithms and lower bounds"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "round compression for parallel graph algorithms in strongly sublinear space"

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)