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: "adwise adaptive window based streaming edge partitioning for high speed graph processing"
  Abstract: In recent years, the graph partitioning problem gained importance as a mandatory preprocessing step for distributed graph processing on very large graphs. Existing graph partitioning algorithms minimize partitioning latency by assigning individual graph edges to partitions in a streaming manner --- at the cost of reduced partitioning quality. However, we argue that the mere minimization of partitioning latency is not the optimal design choice in terms of minimizing total graph analysis latency, i.e., the sum of partitioning and processing latency. Instead, for complex and long-running graph processing algorithms that run on very large graphs, it is beneficial to invest more time into graph partitioning to reach a higher partitioning quality --- which drastically reduces graph processing latency. In this paper, we propose ADWISE, a novel window-based streaming partitioning algorithm that increases the partitioning quality by always choosing the best edge from a set of edges for assignment to a partition. In doing so, ADWISE controls the partitioning latency by adapting the window size dynamically at run-time. Our evaluations show that ADWISE can reach the sweet spot between graph partitioning latency and graph processing latency, reducing the total latency of partitioning plus processing by up to 23-47 percent compared to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "2ps high quality edge partitioning with two phase streaming"):
  with members:
    - "hype massive hypergraph partitioning with neighborhood expansion"
    - "scalable edge partitioning"
    - "a streaming algorithm for graph clustering"
    - "bubing massive crawling for the masses"
    - "defining and evaluating network communities based on ground truth"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-2 (induced by source paper "hype massive hypergraph partitioning with neighborhood expansion"):
  with members:
    - "mesh a flexible distributed hypergraph processing system"
    - "social hash partitioner a scalable distributed hypergraph partitioner"
    - "k way hypergraph partitioning via n level recursive bisection"
    - "spinner scalable graph partitioning in the cloud"

Connected through hyperedge HE-3 (induced by source paper "q graph preserving query locality in multi query graph processing"):
  with members:
    - "weaver a high performance transactional graph database based on refinable timestamps"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "spinner scalable graph partitioning in the cloud"
    - "semantics and complexity of sparql"

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)