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: "computing on masked data a high performance method for improving big data veracity"
  Abstract: The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth “V” is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.

Connected through hyperedge HE-1 (induced by source paper "securing hpc using federated authentication"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "enhancing hpc security with a user based firewall"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-2 (induced by source paper "interactive launch of 16 000 microsoft windows instances on a supercomputer"):
  with members:
    - "the lustre storage architecture"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "scalable system scheduling for hpc and big data"
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "scheduler technologies in support of high performance data analysis"
    - "scalability of vm provisioning systems"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"

Connected through hyperedge HE-3 (induced by source paper "interactive supercomputing on 40 000 cores for machine learning and data analysis"):
  with members:
    - "benchmarking data analysis and machine learning applications on the intel knl many core processor"
    - "scalability of vm provisioning systems"
    - "tensorflow a system for large scale machine learning"
    - "enabling on demand database computing with mit supercloud database management system"
    - "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"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-4 (induced by source paper "measuring the impact of spectre and meltdown"):
  with members:
    - "the lustre storage architecture"
    - "performance measurements of supercomputing and cloud storage solutions"
    - "enhancing hpc security with a user based firewall"
    - "enabling on demand database computing with mit supercloud database management system"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-5 (induced by source paper "lessons learned from a decade of providing interactive on demand high performance computing to scientists and engineers"):
  with members:
    - "mit supercloud portal workspace enabling hpc web application deployment"
    - "scalability of vm provisioning systems"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

Connected through hyperedge HE-6 (induced by source paper "benchmarking data analysis and machine learning applications on the intel knl many core processor"):
  with members:
    - "llmapreduce multi level map reduce for high performance data analysis"
    - "co design of a particle in cell plasma simulation code for intel xeon phi a first look at knights landing"
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-7 (induced by source paper "sok cryptographically protected database search"):
  with members:
    - "associative array model of sql nosql and newsql databases"
    - "the bigdawg polystore system and architecture"
    - "pope partial order preserving encoding"
    - "graphs matrices and the graphblas seven good reasons"
    - "graphulo implementation of server side sparse matrix multiply in the accumulo database"
    - "graphulo linear algebra graph kernels for nosql databases"
    - "a practical oblivious map data structure with secure deletion and history independence"
    - "d4m bringing associative arrays to database engines"

Connected through hyperedge HE-8 (induced by source paper "llmapreduce multi level map reduce for high performance data analysis"):
  with members:
    - "enabling on demand database computing with mit supercloud database management system"
    - "big data strategies for data center infrastructure management using a 3d gaming platform"
    - "d4m bringing associative arrays to database engines"
    - "achieving 100 000 000 database inserts per second using accumulo and d4m"
    - "d4m 2 0 schema a general purpose high performance schema for the accumulo database"

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.CR(Cryptography and Security)