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: "exponentially faster massively parallel maximal matching"
  Abstract: The study of graph problems in the Massively Parallel Computations (MPC) model has recently seen a burst of breakthroughs. Czumaj et al. [STOC'18], Assadi et al. [SODA'19], and Ghaffari et al. [PODC'18], gave algorithms for finding a $1+\varepsilon$ approximate maximum matching in $O(\log \log n)$ rounds using $\widetilde{O}(n)$ memory per machine. Despite this progress, we still have a far more limited understanding of the central symmetry-breaking problem of maximal matching. The round complexity of all these algorithms blows up to $\Omega(\log n)$ in this case, which is considered inefficient. In fact, the only known subpolylogarithmic round algorithm remains to be that of Lattanzi et al. [SPAA'11] which undesirably requires a strictly super-linear space of $n^{1+\Omega(1)}$ per machine. #R##N#In this work, we close this gap by providing a novel analysis of an extremely simple and well-known algorithm. The algorithm edge-samples the graph, partitions the vertices at random, and finds a greedy maximal matching within each partition. We show that this algorithm drastically reduces the vertex degrees. This, among some other results, leads to an $O(\log \log \Delta)$ round algorithm for maximal matching with $O(n)$ space. The space can be further improved to mildly sublinear in $n$ by standard techniques. #R##N#As an immediate corollary, we get a $2$ approximation for minimum vertex cover in essentially the same rounds and space. This is the best possible approximation factor under standard assumptions, culminating a long line of research. Other corollaries include more efficient algorithms for $1 + \varepsilon$ approximate matching and $2 + \varepsilon$ approximate weighted matching. All these results can also be implemented in the congested clique model within $O(\log \log \Delta)$ rounds.

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"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"

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"
    - "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"
    - "weighted matchings via unweighted augmentations"

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"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "congested clique algorithms for graph spanners"

Connected through hyperedge HE-4 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model"):
  with members:
    - "how fast can you update your mst dynamic algorithms for cluster computing"
    - "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"
    - "parallel batch dynamic graph connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "fully dynamic maximal independent set in expected poly log update time"
    - "a deterministic algorithm for the mst problem in constant rounds of congested clique"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "fully dynamic maximal independent set with polylogarithmic update time"

Connected through hyperedge HE-5 (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"
    - "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"
    - "greedy and local ratio algorithms in the mapreduce model"

Connected through hyperedge HE-6 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "batch parallel euler tour trees"
    - "round compression for parallel matching algorithms"

Connected through hyperedge HE-7 (induced by source paper "parallel batch dynamic graphs algorithms and lower bounds"):
  with members:
    - "parallel batch dynamic graph connectivity"
    - "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"
    - "distributed and streaming linear programming in low dimensions"
    - "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"
    - "weighted matchings via unweighted augmentations"

Connected through hyperedge HE-8 (induced by source paper "walking randomly massively and efficiently"):
  with members:
    - "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"
    - "greedy and local ratio algorithms in the mapreduce model"

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)