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: "design principles for sparse matrix multiplication on the gpu"
  Abstract: We implement two novel algorithms for sparse-matrix dense-matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients---(i) merge-based load-balancing and (ii) row-major coalesced memory access---we demonstrate a 3.6x peak speedup and a 23.5% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "fast sparse convnets"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "regularized evolution for image classifier architecture search"
    - "efficient neural audio synthesis"

Connected through hyperedge HE-2 (induced by source paper "a graphblas approach for subgraph counting"):
  with members:
    - "high performance massive subgraph counting using pipelined adaptive group communication"
    - "accelerating pagerank using partition centric processing"
    - "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-3 (induced by source paper "graphblast a high performance linear algebra based graph framework on the gpu"):
  with members:
    - "implementing push pull efficiently in graphblas"
    - "extreme scale de novo metagenome assembly"
    - "junction tree variational autoencoder for molecular graph generation"
    - "gunrock gpu graph analytics"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "mathematical foundations of the graphblas"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-4 (induced by source paper "batched sparse matrix multiplication for accelerating graph convolutional networks"):
  with members:
    - "neural message passing for quantum chemistry"
    - "batched qr and svd algorithms on gpus with applications in hierarchical matrix compression"
    - "gated graph sequence neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-5 (induced by source paper "optimizing the linear fascicle evaluation algorithm for multi core and many core systems"):
  with members:
    - "on optimizing distributed tucker decomposition for dense tensors"
    - "sparse hierarchical tucker factorization and its application to healthcare"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"

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)