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: "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].

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "simple graph coloring algorithms for congested clique and massively parallel computation"):
  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"
    - "improved massively parallel computation algorithms for mis matching and vertex cover"
    - "distributed symmetry breaking algorithms for congested cliques"
    - "sparse matrix multiplication with bandwidth restricted all to all communication"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "mst in o 1 rounds of the congested clique"
    - "simple round compression for parallel vertex cover"

Connected through hyperedge HE-4 (induced by source paper "massively parallel algorithms for finding well connected components in sparse graphs"):
  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"
    - "brief announcement semi mapreduce meets congested clique"
    - "the complexity of delta 1 coloring incongested clique massively parallel computation and centralized local computation"
    - "mis in the congested clique model in o log log δ rounds"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "mst in o 1 rounds of the congested clique"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"

Connected through hyperedge HE-5 (induced by source paper "massively parallel symmetry breaking on sparse graphs mis and maximal matching"):
  with members:
    - "greedy and local ratio algorithms in the mapreduce model"
    - "mst in o 1 rounds of the congested clique"
    - "improved deterministic distributed matching via rounding"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "round compression for parallel matching algorithms"
    - "on estimating maximum matching size in graph streams"
    - "the sparse awakens streaming algorithms for matching size estimation in sparse graphs"
    - "estimating weighted matchings in o n space"

Connected through hyperedge HE-6 (induced by source paper "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"):
  with members:
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "improved massively parallel computation algorithms for mis matching and vertex cover"
    - "parallel graph connectivity in log diameter rounds"
    - "greedy and local ratio algorithms in the mapreduce model"
    - "simple round compression for parallel vertex cover"
    - "an improved distributed algorithm for maximal independent set"
    - "skew in parallel query processing"
    - "lessons from the congested clique applied to mapreduce"

Connected through hyperedge HE-7 (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"
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "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-8 (induced by source paper "massively parallel dynamic programming on trees"):
  with members:
    - "massively parallel symmetry breaking on sparse graphs mis and maximal matching"
    - "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.DS(Data Structures and Algorithms)