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: "accelerating pagerank using partition centric processing"
  Abstract: PageRank is a fundamental link analysis algorithm and a key representative of the performance of other graph algorithms and Sparse Matrix Vector (SpMV) multiplication. Calculating PageRank on sparse graphs generates large amount of random memory accesses resulting in low cache line utilization and poor use of memory bandwidth. In this paper, we present a novel Partition-Centric Processing Methodology (PCPM) that drastically reduces the amount of communication with DRAM and achieves high memory bandwidth. Similar to the state of the art Binning with Vertex-centric Gather-Apply-Scatter (BVGAS) method, PCPM performs partition wise scatter and gather of updates with both phases enjoying full cache line utilization. However, BVGAS suffers from random memory accesses and redundant read/write of update values from nodes to their neighbors. In contrast, PCPM propagates single update from source node to all destinations in a partition, thus decreasing redundancy effectively. We make use of this characteristic to develop a novel bipartite Partition-Node Graph (PNG) data layout for PCPM, that enables streaming memory accesses, with very little generation overhead. We perform detailed analysis of PCPM and provide theoretical bounds on the amount of communication and random DRAM accesses. We experimentally evaluate our approach using 6 large graph datasets and demonstrate an average 2.7x speedup in execution time and 1.7x reduction in communication, compared to the state of the art. We also show that unlike the BVGAS implementation, PCPM is able to take advantage of intelligent node labeling that enhances locality in graphs, by further reducing the amount of communication with DRAM. Although we use PageRank as the target application in this paper, our approach can be applied to generic SpMV computation.

Connected through hyperedge HE-1 (induced by source paper "a graphblas approach for subgraph counting"):
  with members:
    - "design principles for sparse matrix multiplication on the gpu"
    - "high performance massive subgraph counting using pipelined adaptive group communication"
    - "subgraph counting color coding beyond trees"
    - "the reverse cuthill mckee algorithm in distributed memory"
    - "mathematical foundations of the graphblas"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "ringo interactive graph analytics on big memory machines"
    - "graphs matrices and the graphblas seven good reasons"

Connected through hyperedge HE-2 (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"
    - "parallel local graph clustering"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "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-3 (induced by source paper "accurate efficient and scalable graph embedding"):
  with members:
    - "large scale learnable graph convolutional networks"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "benefits of depth in neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "distributed representations of words and phrases and their compositionality"
    - "estimating and sampling graphs with multidimensional random walks"

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)