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: "graph processing on fpgas taxonomy survey challenges"
  Abstract: Graph processing has become an important part of various areas, such as machine learning, computational sciences, medical applications, social network analysis, and many others. Various graphs, for example web or social networks, may contain up to trillions of edges. The sheer size of such datasets, combined with the irregular nature of graph processing, poses unique challenges for the runtime and the consumed power. Field Programmable Gate Arrays (FPGAs) can be an energy-efficient solution to deliver specialized hardware for graph processing. This is reflected by the recent interest in developing various graph algorithms and graph processing frameworks on FPGAs. To facilitate understanding of this emerging domain, we present the first survey and taxonomy on graph computations on FPGAs. Our survey describes and categorizes existing schemes and explains key ideas. Finally, we discuss research and engineering challenges to outline the future of graph computations on FPGAs.

Connected through hyperedge HE-1 (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"
    - "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"
    - "livegraph a transactional graph storage system with purely sequential adjacency list scans"

Connected through hyperedge HE-2 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "scalable approximation algorithm for graph summarization"

Connected through hyperedge HE-3 (induced by source paper "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"):
  with members:
    - "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"
    - "formalising opencypher graph queries in relational algebra"

Connected through hyperedge HE-4 (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"
    - "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"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "active access a mechanism for high performance distributed data centric computations"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "network accelerated non contiguous memory transfers"
    - "transformations of high level synthesis codes for high performance computing"
    - "spin high performance streaming processing in the network"
    - "scaling betweenness centrality using communication efficient sparse matrix multiplication"
    - "enabling highly scalable remote memory access programming with mpi 3 one sided"

Connected through hyperedge HE-6 (induced by source paper "transformations of high level synthesis codes for high performance computing"):
  with members:
    - "parallel programming for fpgas"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "combined spatial and temporal blocking for high performance stencil computation on fpgas using opencl"
    - "finn a framework for fast scalable binarized neural network inference"
    - "programming heterogeneous systems from an image processing dsl"
    - "deep learning on fpgas past present and future"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hitting the memory wall implications of the obvious"

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)