human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"):
  Abstract: Recently, studying fundamental graph problems in the \emph{Massive Parallel Computation (MPC)} framework, inspired by the \emph{MapReduce} paradigm, has gained a lot of attention. A standard assumption, common to most traditional approaches, is to allow $\widetilde{\Omega}(n)$ memory per machine, where $n$ is the number of nodes in the graph and $\widetilde{\Omega}$ hides polylogarithmic factors. However, as pointed out by Karloff et al.~[SODA'10] and Czumaj et al.~[arXiv:1707.03478], it might be unrealistic for a single machine to have linear or only slightly sublinear memory. #R##N#In this paper, we propose the study of a more practical variant of the MPC model which only requires substantially sublinear or even subpolynomial memory per machine. In contrast to the standard MPC model and also streaming, in this low-memory MPC setting, a single machine will only see a small number of nodes in the graph. We introduce a new technique to cope with this imposed locality. #R##N#In particular, we show that the \emph{Maximal Independent Set (MIS)} problem can be solved efficiently, that is, in $\bigO(\log^2 \log n)$ rounds, when the input graph is a tree. This substantially reduces the local memory from $\frac{n}{\poly\log n}$ required by the recent $\bigO(\log \log n)$-round MIS algorithm of Ghaffari et al., to $n^{\eps}$, without incurring a significant loss in the round complexity. Moreover, it demonstrates how to make use of the all-to-all communication in the MPC model to exponentially improve on the corresponding bound in the LOCAL and PRAM models by Lenzen and Wattenhofer [PODC'11].

Includes members:
  Member "breaking the linear memory barrier in mpc fast mis on trees with strongly sublinear memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "faster algorithms for edge connectivity via random 2 out contractions")
  Member "simple graph coloring algorithms for congested clique and massively parallel computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "log diameter rounds algorithms for 2 vertex and 2 edge connectivity")
  Member "matching and mis for uniformly sparse graphs in the low memory mpc model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "batch dynamic algorithms via parallel change propagation and applications to dynamic trees")
  Member "brief announcement semi mapreduce meets congested clique":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph sparsification for derandomizing massively parallel computation with low space")
  Member "round compression for parallel graph algorithms in strongly sublinear space":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic graph algorithms with batch updates in the massively parallel computation model")
  Member "coresets meet edcs algorithms for matching and vertex cover on massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "massively parallel algorithms and hardness for single linkage clustering under ell_p distances":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "fast distributed algorithms for connectivity and mst in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

Question: Please tell me which class the source paper 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)