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: "nscale neighborhood centric large scale graph analytics in the cloud"
  Abstract: There is an increasing interest in executing complex analyses over large graphs, many of which require processing a large number of multi-hop neighborhoods or subgraphs. Examples include ego network analysis, motif counting, personalized recommendations, and others. These tasks are not well served by existing vertex-centric graph processing frameworks, where user programs are only able to directly access the state of a single vertex. This paper introduces NSCALE, a novel end-to-end graph processing framework that enables the distributed execution of complex subgraph-centric analytics over large-scale graphs in the cloud. NSCALE enables users to write programs at the level of subgraphs rather than at the level of vertices. Unlike most previous graph processing frameworks, which apply the user program to the entire graph, NSCALE allows users to declaratively specify subgraphs of interest. Our framework includes a novel graph extraction and packing (GEP) module that utilizes a cost-based optimizer to partition and pack the subgraphs of interest into memory on as few machines as possible. The distributed execution engine then takes over and runs the user program in parallel, while respecting the scope of the various subgraphs. Our experimental results show orders-of-magnitude improvements in performance and drastic reductions in the cost of analytics compared to vertex-centric approaches.

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"
    - "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 "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "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"
    - "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-3 (induced by source paper "kaleido an efficient out of core graph mining system on a single machine"):
  with members:
    - "search space contraction in canonical labeling of graphs"

Connected through hyperedge HE-4 (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"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "pregelix big ger graph analytics on a dataflow engine"
    - "spinning fast iterative data flows"

Connected through hyperedge HE-5 (induced by source paper "q graph preserving query locality in multi query graph processing"):
  with members:
    - "adwise adaptive window based streaming edge partitioning for high speed graph processing"
    - "weaver a high performance transactional graph database based on refinable timestamps"
    - "spinner scalable graph partitioning in the cloud"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-6 (induced by source paper "g thinker big graph mining made easier and faster"):
  with members:
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-7 (induced by source paper "orpheusdb bolt on versioning for relational databases"):
  with members:
    - "principles of dataset versioning exploring the recreation storage tradeoff"
    - "datahub collaborative data science dataset version management at scale"
    - "dbtoaster higher order delta processing for dynamic frequently fresh views"

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.DB(Databases)