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: "the anatomy of big data computing"
  Abstract: Advances in information technology and its widespread growth in several areas of business, engineering, medical and scientific studies are resulting in information/data explosion. Knowledge discovery and decision making from such rapidly growing voluminous data is a challenging task in terms of data organization and processing, which is an emerging trend known as Big Data Computing; a new paradigm which combines large scale compute, new data intensive techniques and mathematical models to build data analytics. Big Data computing demands a huge storage and computing for data curation and processing that could be delivered from on-premise or clouds infrastructures. This paper discusses the evolution of Big Data computing, differences between traditional data warehousing and Big Data, taxonomy of Big Data computing and underpinning technologies, integrated platform of Big Data and Clouds known as Big Data Clouds, layered architecture and components of Big Data Cloud and finally discusses open technical challenges and future directions.

Connected through hyperedge HE-1 (induced by source paper "joint communication computation caching and control in big data multi access edge computing"):
  with members:
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "proactive edge computing in latency constrained fog networks"
    - "a survey on mobile edge computing the communication perspective"
    - "an online secretary framework for fog network formation with minimal latency"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "energy efficient resource allocation for cache assisted mobile edge computing"
    - "network slicing for service oriented networks under resource constraints"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-2 (induced by source paper "internet of things iot and cloud computing enabled disaster management"):
  with members:
    - "cross layer multi cloud real time application qos monitoring and benchmarking as a service framework"
    - "multi cloud resource provisioning with aneka a unified and integrated utilisation of microsoft azure and amazon ec2 instances"
    - "a cloud infrastructure service recommendation system for optimizing real time qos provisioning constraints"
    - "big data computing and clouds"
    - "exploiting social navigation"

Connected through hyperedge HE-3 (induced by source paper "resource management and scheduling for big data applications in cloud computing environments"):
  with members:
    - "optex a deadline aware cost optimization model for spark"
    - "big data computing and clouds"

Connected through hyperedge HE-4 (induced by source paper "a manifesto for future generation cloud computing research directions for the next decade"):
  with members:
    - "cloud benchmarking for maximising performance of scientific applications"
    - "hpc cloud for scientific and business applications taxonomy vision and research challenges"
    - "in datacenter performance analysis of a tensor processing unit"
    - "knowledge infused and consistent complex event processing over real time and persistent streams"
    - "enorm a framework for edge node resource management"
    - "serverless computing current trends and open problems"
    - "demystifying fog computing characterizing architectures applications and abstractions"
    - "snafu function as a service faas runtime design and implementation"

Connected through hyperedge HE-5 (induced by source paper "resource provisioning in science clouds requirements and challenges"):
  with members:
    - "phenomenology tools on cloud infrastructures using openstack"
    - "patience aware scheduling for cloud services freeing users from the chains of boredom"
    - "maximizing cloud providers revenues via energy aware allocation policies"
    - "reliable provisioning of spot instances for compute intensive applications"
    - "workload classification a software energy measurement for efficient scheduling on private cloud platforms"
    - "provisioning spot market cloud resources to create cost effective virtual clusters"
    - "energy efficient management of data center resources for cloud computing a vision architectural elements and open challenges"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-6 (induced by source paper "comparative analysis of spatialhadoop and geospark for geospatial big data analytics"):
  with members:
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "harmonic sum based method for heart rate estimation using ppg signals affected with motion artifacts"
    - "foggis fog computing for geospatial big data analytics"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "a multi smartwatch system for assessing speech characteristics of people with dysarthria in group settings"
    - "fog data enhancing telehealth big data through fog 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)