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: "an nbdmmm algorithm based framework for allocation of resources in cloud"
  Abstract: Cloud computing is a technological advancement in the arena of computing and has taken the utility vision of computing a step further by providing computing resources such as network, storage, compute capacity and servers, as a service via an internet connection. These services are provided to the users in a pay per use manner subjected to the amount of usage of these resources by the cloud users. Since the usage of these resources is done in an elastic manner thus an on demand provisioning of these resources is the driving force behind the entire cloud computing infrastructure therefore the maintenance of these resources is a decisive task that must be taken into account. Eventually, infrastructure level performance monitoring and enhancement is also important. This paper proposes a framework for allocation of resources in a cloud based environment thereby leading to an infrastructure level enhancement of performance in a cloud environment. The framework is divided into four stages Stage 1: Cloud service provider monitors the infrastructure level pattern of usage of resources and behavior of the cloud users. Stage 2: Report the monitoring activities about the usage to cloud service providers. Stage 3: Apply proposed Network Bandwidth Dependent DMMM algorithm .Stage 4: Allocate resources or provide services to cloud users, thereby leading to infrastructure level performance enhancement and efficient management of resources. Analysis of resource usage pattern is considered as an important factor for proper allocation of resources by the service providers, in this paper Google cluster trace has been used for accessing the resource usage pattern in cloud. Experiments have been conducted on cloudsim simulation framework and the results reveal that NBDMMM algorithm improvises allocation of resources in a virtualized cloud.

Connected through hyperedge HE-1 (induced by source paper "resource aware min min ramm algorithm for resource allocation in cloud computing environment"):
  with members:
    - "analysis and clustering of workload in google cluster trace based on resource usage"

Connected through hyperedge HE-2 (induced by source paper "bamhealthcloud a biometric authentication and data management system for healthcare data in cloud"):
  with members:
    - "a cloud based mobile biometric authentication framework"
    - "recent developments in cloud based systems state of art"
    - "adapting resilient propagation for deep learning"

Connected through hyperedge HE-3 (induced by source paper "bamcloud a cloud based mobile biometric authentication framework"):
  with members:
    - "an effective framework for managing university data using a cloud based environment"

Connected through hyperedge HE-4 (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"
    - "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment"

Connected through hyperedge HE-5 (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"
    - "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment"

Connected through hyperedge HE-6 (induced by source paper "analysis and clustering of workload in google cluster trace based on resource usage"):
  with members:
    - "a decision matrix and monitoring based framework for infrastructure performance enhancement in a cloud based environment"

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)