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: "theoretically efficient parallel graph algorithms can be fast and scalable"
  Abstract: There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 13 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We provide a publicly-available benchmark suite containing our implementations.

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"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "the lock free k lsm relaxed priority queue"
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-2 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams"):
  with members:
    - "pangolin a fault tolerant persistent memory programming library"
    - "tracking in order to recover recoverable lock free data structures"
    - "write optimized and consistent rdma based nvm systems"
    - "single machine graph analytics on massive datasets using intel optane dc persistent memory"
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "persistent memory i o primitives"
    - "persistent buffer management with optimistic consistency"
    - "basic performance measurements of the intel optane dc persistent memory module"

Connected through hyperedge HE-3 (induced by source paper "optimal parallel algorithms in the binary forking model"):
  with members:
    - "parallel finger search structures"
    - "parallel write efficient algorithms and data structures for computational geometry"
    - "parallel working set search structures"
    - "parallelism in randomized incremental algorithms"
    - "pam parallel augmented maps"
    - "resource oblivious sorting on multicores"
    - "parallel ordered sets using join"
    - "extending the nested parallel model to the nested dataflow model with provably efficient schedulers"

Connected through hyperedge HE-4 (induced by source paper "parallel algorithms for butterfly computations"):
  with members:
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"
    - "distributed estimation of graph 4 profiles"

Connected through hyperedge HE-5 (induced by source paper "parallel batch dynamic graph connectivity"):
  with members:
    - "parallel working set search structures"
    - "improved worst case deterministic parallel dynamic minimum spanning forest"
    - "pam parallel augmented maps"
    - "batch parallel euler tour trees"
    - "near optimal parallel algorithms for dynamic dfs in undirected graphs"
    - "the ubiquity of large graphs and surprising challenges of graph processing"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "fully dynamic minimum spanning forest with improved worst case update time"

Connected through hyperedge HE-6 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  with members:
    - "gunrock a high performance graph processing library on the gpu"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "tree decompositions and social graphs"
    - "simple fast and scalable reachability oracle"
    - "hub accelerator fast and exact shortest path computation in large social networks"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-7 (induced by source paper "low latency graph streaming using compressed purely functional trees"):
  with members:
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "pam parallel augmented maps"
    - "parallel local graph clustering"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "storing and analyzing historical graph data at scale"
    - "the gap benchmark suite"
    - "efficient snapshot retrieval over historical graph data"

Connected through hyperedge HE-8 (induced by source paper "single machine graph analytics on massive datasets using intel optane dc persistent memory"):
  with members:
    - "system evaluation of the intel optane byte addressable nvm"
    - "basic performance measurements of the intel optane dc persistent memory module"
    - "bigsparse high performance external graph analytics"
    - "early evaluation of intel optane non volatile memory with hpc i o workloads"
    - "the gap benchmark suite"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

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)