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: "benchmarking distributed stream processing platforms for iot applications"
  Abstract: Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, environmental and human systems in real-time. The inherent closed-loop responsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) are becoming essential components of any IoT stack, but the efficacy and performance of contemporary DSPS have not been rigorously studied for IoT data streams and applications. Here, we develop a benchmark suite and performance metrics to evaluate DSPS for streaming IoT applications. The benchmark includes 13 common IoT tasks classified across functional categories and forming micro-benchmarks, and two IoT applications for statistical summarization and predictive analytics that leverage various dataflow patterns of DSPS. These are coupled with stream workloads from real IoT observations on smart cities. We validate the benchmark for the popular Apache Storm DSPS, and present the results.

Connected through hyperedge HE-1 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "building confidential and efficient query services in the cloud with rasp data perturbation"
    - "composition attacks and auxiliary information in data privacy"
    - "a framework for high accuracy privacy preserving mining"

Connected through hyperedge HE-2 (induced by source paper "a survey of benchmarks to evaluate data analytics for smart applications"):
  with members:
    - "towards reliable and efficient job executions in a practical geo distributed data analytics system"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "benchmarking big data systems state of the art and future directions"
    - "context aware computing for the internet of things a survey"
    - "sensing as a service model for smart cities supported by internet of things"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "benchmarking distributed stream data processing systems"):
  with members:
    - "reproducible experiments for comparing apache flink and apache spark on public clouds"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-4 (induced by source paper "model driven scheduling for distributed stream processing systems"):
  with members:
    - "task allocation for distributed stream processing"
    - "r storm resource aware scheduling in storm"
    - "drs dynamic resource scheduling for real time analytics over fast streams"
    - "benchmarking fast data platforms for the aadhaar biometric database"
    - "bigdatabench a big data benchmark suite from internet services"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-5 (induced by source paper "efficient data perturbation for privacy preserving and accurate data stream mining"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "extremal mechanisms for local differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-6 (induced by source paper "benchmarking distributed stream processing engines"):
  with members:
    - "reproducible experiments for comparing apache flink and apache spark on public clouds"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-7 (induced by source paper "learning graphical models from a distributed stream"):
  with members:
    - "communication optimal distributed clustering"
    - "graphical model sketch"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "distributed learning communication complexity and privacy"
    - "randomized algorithms for tracking distributed count frequencies and ranks"
    - "lower bounds for number in hand multiparty communication complexity made easy"

Connected through hyperedge HE-8 (induced by source paper "arm wrestling with big data a study of arm64 and x64 servers for data intensive workloads"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"

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)