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: "flashgraph processing billion node graphs on an array of commodity ssds"
  Abstract: Graph analysis performs many random reads and writes, thus, these workloads are typically performed in memory. Traditionally, analyzing large graphs requires a cluster of machines so the aggregate memory exceeds the graph size. We demonstrate that a multicore server can process graphs with billions of vertices and hundreds of billions of edges, utilizing commodity SSDs with minimal performance loss. We do so by implementing a graph-processing engine on top of a user-space SSD file system designed for high IOPS and extreme parallelism. Our semi-external memory graph engine called FlashGraph stores vertex state in memory and edge lists on SSDs. It hides latency by overlapping computation with I/O. To save I/O bandwidth, FlashGraph only accesses edge lists requested by applications from SSDs; to increase I/O throughput and reduce CPU overhead for I/O, it conservatively merges I/O requests. These designs maximize performance for applications with different I/O characteristics. FlashGraph exposes a general and flexible vertex-centric programming interface that can express a wide variety of graph algorithms and their optimizations. We demonstrate that FlashGraph in semi-external memory performs many algorithms with performance up to 80% of its in-memory implementation and significantly outperforms PowerGraph, a popular distributed in-memory graph engine.

Connected through hyperedge HE-1 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "nxgraph an efficient graph processing system on a single machine"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "pregelix big ger graph analytics on a dataflow engine"

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 "a survey on graph processing accelerators challenges and opportunities"):
  with members:
    - "flashabacus a self governing flash based accelerator for low power systems"
    - "relational inductive biases deep learning and graph networks"
    - "struc2vec learning node representations from structural identity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "democratizing design for future computing platforms"
    - "graph2vec learning distributed representations of graphs"
    - "graphr accelerating graph processing using reram"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-4 (induced by source paper "clusternor a numa optimized clustering framework"):
  with members:
    - "knor a numa optimized in memory distributed and semi external memory k means library"
    - "mllib machine learning in apache spark"
    - "mlpack a scalable c machine learning library"
    - "the anatomy of the facebook social graph"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-5 (induced by source paper "a new frontier for pull based graph processing"):
  with members:
    - "graphit a high performance dsl for graph analytics"
    - "gunrock a high performance graph processing library on the gpu"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-6 (induced by source paper "partitionedvc partitioned external memory graph analytics framework for ssds"):
  with members:
    - "graphmp an efficient semi external memory big graph processing system on a single machine"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-7 (induced by source paper "graphyti a semi external memory graph library for flashgraph"):
  with members:
    - "graphmp an efficient semi external memory big graph processing system on a single machine"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-8 (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"

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.DC(Distributed, Parallel, and Cluster Computing)