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: "drs dynamic resource scheduling for real time analytics over fast streams"
  Abstract: In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.

Connected through hyperedge HE-1 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks"):
  with members:
    - "fog assisted caching employing solar renewable energy for delivering video on demand service"
    - "energy efficient and resilient infrastructure for fog computing health monitoring applications"
    - "virtual machines embedding for cloud pon awgr and server based data centres"
    - "a survey on geographically distributed big data processing using mapreduce"
    - "disaggregation for improved efficiency in fog computing era"
    - "experimental evaluation of server centric passive optical network based data centre architecture"
    - "energy efficient distributed processing in vehicular cloud architecture"
    - "energy efficient software matching in vehicular fog"

Connected through hyperedge HE-2 (induced by source paper "model driven scheduling for distributed stream processing systems"):
  with members:
    - "benchmarking distributed stream processing platforms for iot applications"
    - "task allocation for distributed stream processing"
    - "r storm resource aware scheduling in storm"
    - "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-3 (induced by source paper "auto tuning distributed stream processing systems using reinforcement learning"):
  with members:
    - "deep learning in mobile and wireless networking a survey"
    - "deep reinforcement learning for multi resource multi machine job scheduling"
    - "faster discovery of faster system configurations with spectral learning"
    - "tensorflow a system for large scale machine learning"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-4 (induced by source paper "henge intent driven multi tenant stream processing"):
  with members:
    - "slaq quality driven scheduling for distributed machine learning"
    - "r storm resource aware scheduling in storm"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-5 (induced by source paper "trevor automatic configuration and scaling of stream processing pipelines"):
  with members:
    - "hemingway modeling distributed optimization algorithms"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-6 (induced by source paper "parallel stream processing against workload skewness and variance"):
  with members:
    - "optimal operator state migration for elastic data stream processing"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-7 (induced by source paper "integrative dynamic reconfiguration in a parallel stream processing engine"):
  with members:
    - "the power of both choices practical load balancing for distributed stream processing engines"

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)