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: "mathematical foundations of the graphblas"
  Abstract: The GraphBLAS standard (GraphBlas.org) is being developed to bring the potential of matrix-based graph algorithms to the broadest possible audience. Mathematically, the GraphBLAS defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the mathematics of the GraphBLAS. Graphs represent connections between vertices with edges. Matrices can represent a wide range of graphs using adjacency matrices or incidence matrices. Adjacency matrices are often easier to analyze while incidence matrices are often better for representing data. Fortunately, the two are easily connected by matrix multiplication. A key feature of matrix mathematics is that a very small number of matrix operations can be used to manipulate a very wide range of graphs. This composability of a small number of operations is the foundation of the GraphBLAS. A standard such as the GraphBLAS can only be effective if it has low performance overhead. Performance measurements of prototype GraphBLAS implementations indicate that the overhead is low.

Connected through hyperedge HE-1 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices"):
  with members:
    - "streaming 1 9 billion hypersparse network updates per second with d4m"
    - "hypersparse neural network analysis of large scale internet traffic"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "streaming graph challenge stochastic block partition"
    - "static graph challenge subgraph isomorphism"
    - "benchmarking scidb data import on hpc systems"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-2 (induced by source paper "ai data wrangling with associative arrays"):
  with members:
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-4 (induced by source paper "knowledge based conversational search"):
  with members:
    - "message passing for complex question answering over knowledge graphs"
    - "query expansion techniques for information retrieval a survey"
    - "open data chatbot"
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-5 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  with members:
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "active access a mechanism for high performance distributed data centric computations"
    - "network accelerated non contiguous memory transfers"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "high level programming abstractions for distributed graph processing"

Connected through hyperedge HE-6 (induced by source paper "message passing for complex question answering over knowledge graphs"):
  with members:
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "magnitude a fast efficient universal vector embedding utility package"
    - "embedding logical queries on knowledge graphs"
    - "relational inductive biases deep learning and graph networks"
    - "learning to rank query graphs for complex question answering over knowledge graphs"
    - "no one is perfect analysing the performance of question answering components over the dbpedia knowledge graph"

Connected through hyperedge HE-7 (induced by source paper "slim fly a cost effective low diameter network topology"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "network accelerated non contiguous memory transfers"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "slim graph practical lossy graph compression for approximate graph processing storage and analytics"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-8 (induced by source paper "graph processing on fpgas taxonomy survey challenges"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "high level programming abstractions for distributed graph processing"
    - "an efficient dispatcher for large scale graphprocessing on opencl based fpgas"
    - "an efficient graph accelerator with parallel data conflict management"
    - "nxgraph an efficient graph processing system on a single machine"

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.MS(Mathematical Software)