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: "streaming graph challenge stochastic block partition"
  Abstract: An important objective for analyzing real-world graphs is to achieve scalable performance on large, streaming graphs. A challenging and relevant example is the graph partition problem. As a combinatorial problem, graph partition is NP-hard, but existing relaxation methods provide reasonable approximate solutions that can be scaled for large graphs. Competitive benchmarks and challenges have proven to be an effective means to advance state-of-the-art performance and foster community collaboration. This paper describes a graph partition challenge with a baseline partition algorithm of sub-quadratic complexity. The algorithm employs rigorous Bayesian inferential methods based on a statistical model that captures characteristics of the real-world graphs. This strong foundation enables the algorithm to address limitations of well-known graph partition approaches such as modularity maximization. This paper describes various aspects of the challenge including: (1) the data sets and streaming graph generator, (2) the baseline partition algorithm with pseudocode, (3) an argument for the correctness of parallelizing the Bayesian inference, (4) different parallel computation strategies such as node-based parallelism and matrix-based parallelism, (5) evaluation metrics for partition correctness and computational requirements, (6) preliminary timing of a Python-based demonstration code and the open source C++ code, and (7) considerations for partitioning the graph in streaming fashion. Data sets and source code for the algorithm as well as metrics, with detailed documentation are available at GraphChallenge.org.

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"
    - "static graph challenge subgraph isomorphism"
    - "benchmarking scidb data import on hpc systems"
    - "mathematical foundations of the graphblas"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"

Connected through hyperedge HE-2 (induced by source paper "sparse deep neural network graph challenge"):
  with members:
    - "radix net structured sparse matrices for deep neural networks"
    - "sparse deep neural network exact solutions"
    - "in datacenter performance analysis of a tensor processing unit"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "static graph challenge subgraph isomorphism"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-3 (induced by source paper "streaming 1 9 billion hypersparse network updates per second with d4m"):
  with members:
    - "hyperscaling internet graph analysis with d4m on the mit supercloud"
    - "static graph challenge subgraph isomorphism"
    - "polystore mathematics of relational algebra"
    - "enabling massive deep neural networks with the graphblas"
    - "associative array model of sql nosql and newsql databases"
    - "benchmarking scidb data import on hpc systems"
    - "lustre hadoop accumulo"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"

Connected through hyperedge HE-4 (induced by source paper "a fast and efficient incremental approach toward dynamic community detection"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "design generation and validation of extreme scale power law graphs"
    - "parallel heuristics for scalable community detection"
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "generalized louvain method for community detection in large networks"

Connected through hyperedge HE-5 (induced by source paper "redisgraph graphblas enabled graph database"):
  with members:
    - "static graph challenge subgraph isomorphism"
    - "novel graph processor architecture prototype system and results"
    - "mathematical foundations of the graphblas"
    - "standards for graph algorithm primitives"

Connected through hyperedge HE-6 (induced by source paper "design generation and validation of extreme scale power law graphs"):
  with members:
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "constructing adjacency arrays from incidence arrays"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"

Connected through hyperedge HE-7 (induced by source paper "graphchallenge org raising the bar on graph analytic performance"):
  with members:
    - "distributed triangle counting in the graphulo matrix math library"
    - "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv"
    - "an ensemble framework for detecting community changes in dynamic networks"
    - "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections"
    - "static graph challenge subgraph isomorphism"
    - "enabling massive deep neural networks with the graphblas"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-8 (induced by source paper "static graph challenge subgraph isomorphism"):
  with members:
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "using a power law distribution to describe big data"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "imagenet large scale visual recognition challenge"
    - "truss decomposition in massive networks"
    - "community structure and scale free collections of erdős renyi graphs"

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)