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: "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment"
  Abstract: Cloud environment is very different from traditional computing environment and therefore tracking the performance of cloud leverages additional requirements. The movement of data in cloud is very fast. Hence, it requires that resources and infrastructure available at disposal must be equally competent. Infrastructure level performance in cloud involves the performance of servers, network and storage which act as the heart and soul for driving the entire cloud business. Thus a constant improvement and enhancement of infrastructure level performance is an important task that needs to be taken into account. This paper proposes a framework for infrastructure performance enhancement in a cloud based environment. The framework is broadly divided into four steps: a) Infrastructure level monitoring of usage pattern and behaviour of the cloud end users, b) Reporting of the monitoring activities to the cloud service provider c) Cloud service provider assigns priority according to our decision matrix based max-min algorithm (DMMM) d) Providing services to cloud users leading to infrastructure performance enhancement. Our framework is based on decision matrix and monitoring in cloud using our proposed decision matrix based max-min algorithm, which draws its inspiration from the original min-min algorithm. This algorithm makes use of decision matrix to make decisions regarding distribution of resources among the cloud users.

Connected through hyperedge HE-1 (induced by source paper "exploiting data reduction principles in cloud based data management for cryo image data"):
  with members:
    - "recent developments in cloud based systems state of art"
    - "monetary cost optimizations for hosting workflow as a service in iaas clouds"

Connected through hyperedge HE-2 (induced by source paper "exploring non homogeneity and dynamicity of high scale cloud through hive and pig"):
  with members:
    - "analysis and clustering of workload in google cluster trace based on resource usage"
    - "an effective framework for managing university data using a cloud based environment"
    - "recent developments in cloud based systems state of art"
    - "an nbdmmm algorithm based framework for allocation of resources in cloud"

Connected through hyperedge HE-3 (induced by source paper "an effective framework for managing university data using a cloud based environment"):
  with members:
    - "analysis and clustering of workload in google cluster trace based on resource usage"
    - "recent developments in cloud based systems state of art"
    - "an nbdmmm algorithm based framework for allocation of resources in cloud"

Connected through hyperedge HE-4 (induced by source paper "dengue disease prediction using weka data mining tool"):
  with members:
    - "an effective framework for managing university data using a cloud based environment"
    - "alternating decision trees for early diagnosis of dengue fever"

Connected through hyperedge HE-5 (induced by source paper "analysis and clustering of workload in google cluster trace based on resource usage"):
  with members:
    - "an nbdmmm algorithm based framework for allocation of resources in cloud"

Connected through hyperedge HE-6 (induced by source paper "seeking black lining in cloud"):
  with members:
    - "analysis and clustering of workload in google cluster trace based on resource usage"

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)