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: "survey on improved scheduling in hadoop mapreduce in cloud environments"
  Abstract: Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. In this paper we study various scheduler improvements possible with Hadoop and also provided some guidelines on how to improve the scheduling in Hadoop in Cloud Environments.

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 "big data meet cyber physical systems a panoramic survey"):
  with members:
    - "information and communications technologies for sustainable development goals state of the art needs and perspectives"
    - "improving the coverage and spectral efficiency of millimeter wave cellular networks using device to device relays"
    - "developing an edge computing platform for real time descriptive analytics"
    - "security and privacy aspects in mapreduce on clouds a survey"
    - "wireless device to device caching networks basic principles and system performance"
    - "social computing for mobile big data"
    - "streaming big data meets backpressure in distributed network computation"
    - "big data caching for networking moving from cloud to edge"

Connected through hyperedge HE-3 (induced by source paper "performance evaluation of job schedulers on hadoop yarn"):
  with members:
    - "hybrid job driven scheduling for virtual mapreduce clusters"

Connected through hyperedge HE-4 (induced by source paper "d space4cloud a design tool for big data applications"):
  with members:
    - "hadoop on hpc integrating hadoop and pilot based dynamic resource management"
    - "a tale of two data intensive paradigms applications abstractions and architectures"

Connected through hyperedge HE-5 (induced by source paper "running genetic algorithms on hadoop for solving high dimensional optimization problems"):
  with members:
    - "a framework for genetic algorithms based on hadoop"

Connected through hyperedge HE-6 (induced by source paper "scheduling data intensive workloads through virtualization on mapreduce based clouds"):
  with members:
    - "performance issues of heterogeneous hadoop clusters in cloud computing"

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)