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: "goffish a sub graph centric framework for large scale graph analytics"
  Abstract: Vertex centric models for large scale graph processing are gaining traction due to their simple distributed programming abstraction. However, pure vertex centric algorithms under-perform due to large communication overheads and slow iterative convergence. We introduce GoFFish a scalable sub-graph centric framework co-designed with a distributed persistent graph storage for large scale graph analytics on commodity clusters, offering the added natural flexibility of shared memory sub-graph computation. We map Connected Components, SSSP and PageRank algorithms to this model and empirically analyze them for several real world graphs, demonstrating orders of magnitude improvements, in some cases, compared to Apache Giraph’s vertex centric framework.

Connected through hyperedge HE-1 (induced by source paper "a distributed path query engine for temporal property graphs"):
  with members:
    - "fake news detection on social media a data mining perspective"
    - "distributed programming over time series graphs"

Connected through hyperedge HE-2 (induced by source paper "a partition centric distributed algorithm for identifying euler circuits in large graphs"):
  with members:
    - "a meta graph approach to analyze subgraph centric distributed programming models"
    - "gunrock a high performance graph processing library on the gpu"
    - "parallel graph partitioning for complex networks"

Connected through hyperedge HE-3 (induced by source paper "partitioning graphs for the cloud using reinforcement learning"):
  with members:
    - "gunrock a high performance graph processing library on the gpu"
    - "spinner scalable graph partitioning in the cloud"

Connected through hyperedge HE-4 (induced by source paper "towards semantic big graph analytics for cross domain knowledge discovery"):
  with members:
    - "detection of surgical site infection utilizing automated feature generation in clinical notes"
    - "sp2bench a sparql performance benchmark"

Connected through hyperedge HE-5 (induced by source paper "distributed algorithms for subgraph centric graph platforms"):
  with members:
    - "elastic partition placement for non stationary graph algorithms"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-6 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  with members:
    - "partitionedvc partitioned external memory graph analytics framework for ssds"
    - "graphit a high performance dsl for graph analytics"
    - "accelerating pagerank using partition centric processing"
    - "parallel local graph clustering"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-7 (induced by source paper "high level programming abstractions for distributed graph processing"):
  with members:
    - "the gremlin graph traversal machine and language invited talk"
    - "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"
    - "spinning fast iterative data flows"

Connected through hyperedge HE-8 (induced by source paper "drone a distributed subgraph centric framework for processing large scale power law graphs"):
  with members:
    - "high level programming abstractions for distributed graph processing"
    - "distributed edge partitioning for graph processing"

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)