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: "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
  Abstract: The success of massively parallel computation (MPC) paradigms such as MapReduce has led to a significant interest in better understanding their true computational power. The fundamental question in this regard is how the advantages of this model (e.g. free local computation) can be leveraged to improve the round-complexity of algorithms inherited from traditional parallel and distributed models such as PRAM or LOCAL. Problems such as maximal independent set (MIS) or maximal matching are among the most intensively studied problems in the field and logarithmic round algorithms have been known for these problems from 1980s. However, prior to our work, no sublogarithmic MPC algorithm was known for these problems using a truly sublinear space of $n^{1-\Omega(1)}$ per machine where $n$ denotes the number of vertices. #R##N#Our main result is a truly sublinear algorithm that takes $O(\log \alpha + \log^2\log n)$ rounds to compute MIS or maximal matching using an optimal total space of $\tilde{O}(m)$ where $m$ denotes the number of edges and $\alpha$ denotes the arboricity of the input graph. We believe parametrization by arboricity is particularly interesting for this regime of MPC since most families of sparse graphs have a small arboricity. Our algorithms do not assume arboricity is constant and do not require to be given $\alpha$. This is the first substantial improvement over the known PRAM/LOCAL algorithms for these problems on such a wide class of graphs. #R##N#Since trees have arboricity one, our algorithm improves and generalizes the recent algorithm of Brandt et al.~[arXiv:1802.06748] that finds MIS on trees in $O(\log^3\log n)$ rounds. Moreover, the $n$-dependency of our algorithm exponentially improves over the corresponding $O(\log \alpha + \sqrt{\log n})$ PRAM/LOCAL bounds by Barenboim et al.~[FOCS'12] and Ghaffari~[SODA'16].

Connected through hyperedge HE-1 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"):
  with members:
    - "simple concurrent labeling algorithms for connected components"
    - "simple graph coloring algorithms for congested clique and massively parallel computation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "parallel graph connectivity in log diameter rounds"
    - "brief announcement semi mapreduce meets congested clique"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "mst in o 1 rounds of the congested clique"

Connected through hyperedge HE-2 (induced by source paper "near optimal massively parallel graph connectivity"):
  with members:
    - "connected components on a pram in log diameter time"
    - "graph connectivity in log diameter steps using label propagation"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "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 components at scale via local contractions"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"

Connected through hyperedge HE-3 (induced by source paper "exponentially faster massively parallel maximal matching"):
  with members:
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "correlation clustering in data streams"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-4 (induced by source paper "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-5 (induced by source paper "weighted matchings via unweighted augmentations"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-6 (induced by source paper "round compression for parallel graph algorithms in strongly sublinear space"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "mst in o 1 rounds of the congested clique"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "simple round compression for parallel vertex cover"
    - "an improved distributed algorithm for maximal independent set"

Connected through hyperedge HE-7 (induced by source paper "massively parallel dynamic programming on trees"):
  with members:
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "communication steps for parallel query processing"
    - "densest subgraph in streaming and mapreduce"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "sorting searching and simulation in the mapreduce framework"

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.DC(Distributed, Parallel, and Cluster Computing)