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: "using a power law distribution to describe big data"
  Abstract: The gap between data production and user ability to access, compute and produce meaningful results calls for tools that address the challenges associated with big data volume, velocity and variety. One of the key hurdles is the inability to methodically remove expected or uninteresting elements from large data sets. This difficulty often wastes valuable researcher and computational time by expending resources on uninteresting parts of data. Social sensors, or sensors which produce data based on human activity, such as Wikipedia, Twitter, and Facebook have an underlying structure which can be thought of as having a Power Law distribution. Such a distribution implies that few nodes generate large amounts of data. In this article, we propose a technique to take an arbitrary dataset and compute a power law distributed background model that bases its parameters on observed statistics. This model can be used to determine the suitability of using a power law or automatically identify high degree nodes for filtering and can be scaled to work with big data.

Connected through hyperedge HE-1 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "this looks like that deep learning for interpretable image recognition"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-2 (induced by source paper "design generation and validation of extreme scale power law graphs"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "graphchallenge org raising the bar on graph analytic performance"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "hyperscaling internet graph analysis with d4m on the mit supercloud"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "associative array model of sql nosql and newsql databases"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "tensorflow a system for large scale machine learning"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-5 (induced by source paper "d4m 3 0 extended database and language capabilities"):
  with members:
    - "benchmarking the graphulo processing framework"
    - "the bigdawg polystore system and architecture"
    - "julia implementation of the dynamic distributed dimensional data model"
    - "benchmarking scidb data import on hpc systems"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "enabling on demand database computing with mit supercloud database management system"
    - "graphulo linear algebra graph kernels for nosql databases"

Connected through hyperedge HE-6 (induced by source paper "distributed triangle counting in the graphulo matrix math library"):
  with members:
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "static graph challenge subgraph isomorphism"
    - "benchmarking the graphulo processing framework"
    - "hypergraph partitioning for sparse matrix matrix multiplication"
    - "pipegen data pipe generator for hybrid analytics"
    - "the bigdawg polystore system and architecture"
    - "exploiting multiple levels of parallelism in sparse matrix matrix multiplication"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"

Connected through hyperedge HE-7 (induced by source paper "laradb a minimalist kernel for linear and relational algebra computation"):
  with members:
    - "lara a key value algebra underlying arrays and relations"
    - "associative array model of sql nosql and newsql databases"
    - "mathematical foundations of the graphblas"
    - "from nosql accumulo to newsql graphulo design and utility of graph algorithms inside a bigtable database"
    - "mllib machine learning in apache spark"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "big data dimensional analysis"

Connected through hyperedge HE-8 (induced by source paper "static graph challenge subgraph isomorphism"):
  with members:
    - "streaming graph challenge stochastic block partition"
    - "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms"
    - "a comparative study on exact triangle counting algorithms on the gpu"
    - "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.SI(Social and Information Networks)