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: "spinner scalable graph partitioning in the cloud"
  Abstract: Several organizations, like social networks, store and routinely analyze large graphs as part of their daily operation. Such graphs are typically distributed across multiple servers, and graph partitioning is critical for efficient graph management. Existing partitioning algorithms focus on finding graph partitions with good locality, but disregard the pragmatic challenges of integrating partitioning into large-scale graph management systems deployed on the cloud, such as dealing with the scale and dynamicity of the graph and the compute environment. #R##N#In this paper, we propose Spinner, a scalable and adaptive graph partitioning algorithm based on label propagation designed on top of the Pregel model. Spinner scales to massive graphs, produces partitions with locality and balance comparable to the state-of-the-art and efficiently adapts the partitioning upon changes. We describe our algorithm and its implementation in the Pregel programming model that makes it possible to partition billion-vertex graphs. We evaluate Spinner with a variety of synthetic and real graphs and show that it can compute partitions with quality comparable to the state-of-the art. In fact, by using Spinner in conjunction with the Giraph graph processing engine, we speed up different applications by a factor of 2 relative to standard hash partitioning.

Connected through hyperedge HE-1 (induced by source paper "multi dimensional balanced graph partitioning via projected gradient descent"):
  with members:
    - "vebo a vertex and edge balanced ordering heuristic to load balance parallel graph processing"
    - "matrix completion has no spurious local minimum"
    - "compressing graphs and indexes with recursive graph bisection"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "distributed balanced partitioning via linear embedding"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "partitioning complex networks via size constrained clustering"
    - "nonuniform graph partitioning with unrelated weights"

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"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "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 "partitioning graphs for the cloud using reinforcement learning"):
  with members:
    - "gunrock a high performance graph processing library on the gpu"
    - "goffish a sub graph centric framework for large scale graph analytics"

Connected through hyperedge HE-4 (induced by source paper "mesh a flexible distributed hypergraph processing system"):
  with members:
    - "dynamic shortest path algorithms for hypergraphs"

Connected through hyperedge HE-5 (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"
    - "adwise adaptive window based streaming edge partitioning for high speed graph processing"
    - "k way hypergraph partitioning via n level recursive bisection"

Connected through hyperedge HE-6 (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"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-7 (induced by source paper "partitioning ska dataflows for optimal graph execution"):
  with members:
    - "daliuge a graph execution framework for harnessing the astronomical data deluge"

Connected through hyperedge HE-8 (induced by source paper "adwise adaptive window based streaming edge partitioning for high speed graph processing"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)