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: "round compression for parallel matching algorithms"
  Abstract: For over a decade now we have been witnessing the success of {\em massive parallel computation} (MPC) frameworks, such as MapReduce, Hadoop, Dryad, or Spark. One of the reasons for their success is the fact that these frameworks are able to accurately capture the nature of large-scale computation. In particular, compared to the classic distributed algorithms or PRAM models, these frameworks allow for much more local computation. The fundamental question that arises in this context is though: can we leverage this additional power to obtain even faster parallel algorithms? #R##N#A prominent example here is the {\em maximum matching} problem---one of the most classic graph problems. It is well known that in the PRAM model one can compute a 2-approximate maximum matching in $O(\log{n})$ rounds. However, the exact complexity of this problem in the MPC framework is still far from understood. Lattanzi et al. showed that if each machine has $n^{1+\Omega(1)}$ memory, this problem can also be solved $2$-approximately in a constant number of rounds. These techniques, as well as the approaches developed in the follow up work, seem though to get stuck in a fundamental way at roughly $O(\log{n})$ rounds once we enter the near-linear memory regime. It is thus entirely possible that in this regime, which captures in particular the case of sparse graph computations, the best MPC round complexity matches what one can already get in the PRAM model, without the need to take advantage of the extra local computation power. #R##N#In this paper, we finally refute that perplexing possibility. That is, we break the above $O(\log n)$ round complexity bound even in the case of {\em slightly sublinear} memory per machine. In fact, our improvement here is {\em almost exponential}: we are able to deliver a $(2+\epsilon)$-approximation to maximum matching, for any fixed constant $\epsilon>0$, in $O((\log \log n)^2)$ 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"
    - "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 "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"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"

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"
    - "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-6 (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"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "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-7 (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"
    - "exponentially faster massively parallel maximal matching"
    - "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"

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)