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: "effective techniques for message reduction and load balancing in distributed graph computation"
  Abstract: Massive graphs, such as online social networks and communication networks, have become common today. To efficiently analyze such large graphs, many distributed graph computing systems have been developed. These systems employ the "think like a vertex" programming paradigm, where a program proceeds in iterations and at each iteration, vertices exchange messages with each other. However, using Pregel's simple message passing mechanism, some vertices may send/receive significantly more messages than others due to either the high degree of these vertices or the logic of the algorithm used. This forms the communication bottleneck and leads to imbalanced workload among machines in the cluster. In this paper, we propose two effective message reduction techniques: (1)vertex mirroring with message combining, and (2)an additional request-respond API. These techniques not only reduce the total number of messages exchanged through the network, but also bound the number of messages sent/received by any single vertex. We theoretically analyze the effectiveness of our techniques, and implement them on top of our open-source Pregel implementation called Pregel+. Our experiments on various large real graphs demonstrate that our message reduction techniques significantly improve the performance of distributed graph computation.

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"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-2 (induced by source paper "distributed edge partitioning for trillion edge graphs"):
  with members:
    - "social hash partitioner a scalable distributed hypergraph partitioner"
    - "partitioning trillion edge graphs in minutes"
    - "spinner scalable graph partitioning in the cloud"
    - "recent advances in graph partitioning"
    - "defining and evaluating network communities based on ground truth"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "a case for stale synchronous distributed model for declarative recursive computation"):
  with members:
    - "scaling up reasoning and advanced analytics on bigdata"
    - "parallel correctness and transferability for conjunctive queries"
    - "a datalog based computational model for coordination free data parallel systems"
    - "relational transducers for declarative networking"

Connected through hyperedge HE-4 (induced by source paper "graphmp i o efficient big graph analytics on a single commodity machine"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "graphh high performance big graph analytics in small clusters"
    - "graphmp an efficient semi external memory big graph processing system on a single machine"
    - "gunrock a high performance graph processing library on the gpu"
    - "flashgraph processing billion node graphs on an array of commodity ssds"
    - "pregelix big ger graph analytics on a dataflow engine"

Connected through hyperedge HE-5 (induced by source paper "high level programming abstractions for distributed graph processing"):
  with members:
    - "the gremlin graph traversal machine and language invited talk"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "pregelix big ger graph analytics on a dataflow engine"
    - "spinning fast iterative data flows"

Connected through hyperedge HE-6 (induced by source paper "composing optimization techniques for vertex centric graph processing via communication channels"):
  with members:
    - "goffish a sub graph centric framework for large scale graph analytics"

Connected through hyperedge HE-7 (induced by source paper "load balancing for skewed streams on heterogeneous clusters"):
  with members:
    - "consistent hashing with bounded loads"
    - "partial key grouping load balanced partitioning of distributed streams"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-8 (induced by source paper "g thinker big graph mining made easier and faster"):
  with members:
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
    - "distributed graphlab a framework for machine learning in the cloud"

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)