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: "graphblast a high performance linear algebra based graph framework on the gpu"
  Abstract: Author(s): Yang, Carl; Buluc, Aydin; Owens, John D | Abstract: High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs, because of three challenges: (1) difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based in sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first open-source linear algebra-based graph framework on GPU targeting high-performance computing. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

Connected through hyperedge HE-1 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "large scale learnable graph convolutional networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "jgrapht a java library for graph data structures and algorithms"):
  with members:
    - "mathematical foundations of the graphblas"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "gunrock a high performance graph processing library on the gpu"
    - "networkit a tool suite for large scale complex network analysis"
    - "pagerank beyond the web"
    - "malware classification based on call graph clustering"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-3 (induced by source paper "fastsv a distributed memory connected component algorithm with fast convergence"):
  with members:
    - "composing optimization techniques for vertex centric graph processing via communication channels"
    - "an adaptive parallel algorithm for computing connected components"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "finding connected components on map reduce in logarithmic rounds"
    - "parallel breadth first search on distributed memory systems"

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)