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: "greedy sequential maximal independent set and matching are parallel on average"
  Abstract: The greedy sequential algorithm for maximal independent set (MIS) loops over the vertices in arbitrary order adding a vertex to the resulting set if and only if no previous neighboring vertex has been added. In this loop, as in many sequential loops, each iterate will only depend directly on a subset of the previous iterates (i.e. knowing that any one of a vertices neighbors is in the MIS or knowing that it has no previous neighbors is sufficient to decide its fate). This leads to a dependence structure among the iterates. If this structure is shallow then running the iterates in parallel while respecting the dependencies can lead to an efficient parallel implementation mimicking the sequential algorithm. #R##N#In this paper, we show that for any graph, and for a random ordering of the vertices, the dependence depth of the sequential greedy MIS algorithm is polylogarithmic (O(log^2 n) with high probability). Our results extend previous results that show polylogarithmic bounds only for random graphs. We show similar results for a greedy maximal matching (MM). For both problems we describe simple linear work parallel algorithms based on the approach. The algorithms allow for a smooth tradeoff between more parallelism and reduced work, but always return the same result as the sequential greedy algorithms. We present experimental results that demonstrate efficiency and the tradeoff between work and parallelism.

Connected through hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "the lock free k lsm relaxed priority queue"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-2 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms"):
  with members:
    - "smaller cuts higher lower bounds"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "tight bounds on the round complexity of the distributed maximum coverage problem"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-3 (induced by source paper "fully dynamic maximal independent set with polylogarithmic update time"):
  with members:
    - "fully dynamic maximal independent set in expected poly log update time"
    - "exponentially faster massively parallel maximal matching"
    - "simple dynamic algorithms for maximal independent set and other problems"
    - "fully dynamic mis in uniformly sparse graphs"
    - "fully dynamic maximal independent set with sublinear update time"
    - "improved algorithms for fully dynamic maximal independent set"
    - "correlation clustering in data streams"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"

Connected through hyperedge HE-4 (induced by source paper "fully dynamic matching beating 2 approximation in delta epsilon update time"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "correlation clustering in data streams"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "fully dynamic approximate maximum matching and minimum vertex cover in o log 3 n worst case update time"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "deterministic fully dynamic approximate vertex cover and fractional matching in o 1 amortized update time"
    - "fully dynamic maximal matching in constant update time"
    - "new deterministic approximation algorithms for fully dynamic matching"

Connected through hyperedge HE-5 (induced by source paper "massively parallel computation via remote memory access"):
  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"
    - "connected components at scale via local contractions"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "massively parallel algorithms and hardness for single linkage clustering under ell_p distances"
    - "communication steps for parallel query processing"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-6 (induced by source paper "improved massively parallel computation algorithms for mis matching and vertex cover"):
  with members:
    - "correlation clustering in data streams"
    - "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"
    - "round compression for parallel matching algorithms"
    - "derandomizing local distributed algorithms under bandwidth restrictions"
    - "deterministic mst sparsification in the congested clique"
    - "algebraic methods in the congested clique"

Connected through hyperedge HE-7 (induced by source paper "theoretically efficient parallel graph algorithms can be fast and scalable"):
  with members:
    - "single source bottleneck path algorithm faster than sorting for sparse graphs"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "parallelism in randomized incremental algorithms"
    - "bigsparse high performance external graph analytics"
    - "implicit decomposition for write efficient connectivity algorithms"
    - "nearly work efficient parallel algorithm for digraph reachability"
    - "a new efficient construction on probabilistic tree embeddings"
    - "compressing graphs and indexes with recursive graph bisection"

Connected through hyperedge HE-8 (induced by source paper "fully dynamic mis in uniformly sparse graphs"):
  with members:
    - "fully dynamic maximal independent set in expected poly log update time"
    - "fully dynamic maximal independent set with polylogarithmic update time"
    - "fully dynamic maximal independent set with sublinear update time"
    - "optimal dynamic distributed mis"
    - "orienting fully dynamic graphs with worst case time bounds"
    - "simple deterministic algorithms for fully dynamic maximal matching"

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)