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: "boosting java performance using gpgpus"
  Abstract: Heterogeneous programming has started becoming the norm in order to achieve better performance by running portions of code on the most appropriate hardware resource. Currently, significant engineering efforts are undertaken in order to enable existing programming languages to perform heterogeneous execution mainly on GPUs. In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. By using the Jacc framework, developers have the ability to add GPGPU support into their applications with minimal code refactoring. #R##N#To simplify the development of GPGPU applications we allow developers to model heterogeneous code using two key abstractions: \textit{tasks}, which encapsulate all the information needed to execute code on a GPGPU; and \textit{task graphs}, which capture the inter-task control-flow of the application. Using this information the Jacc runtime is able to automatically handle data movement and synchronization between the host and the GPGPU; eliminating the need for explicitly managing disparate memory spaces. #R##N#In order to generate highly parallel GPGPU code, Jacc provides developers with the ability to decorate key aspects of their code using annotations. The compiler, in turn, exploits this information in order to automatically generate code without requiring additional code refactoring. #R##N#Finally, we demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 32x and a 4.4x code decrease across eight evaluated benchmarks on a NVIDIA Tesla K20m GPU.

Connected through hyperedge HE-1 (induced by source paper "effective extensible programming unleashing julia on gpus"):
  with members:
    - "julia a fresh approach to numerical computing"
    - "julia a fast dynamic language for technical computing"

Connected through hyperedge HE-2 (induced by source paper "navigating the landscape for real time localization and mapping for robotics and virtual and augmented reality"):
  with members:
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "in datacenter performance analysis of a tensor processing unit"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "comparative design space exploration of dense and semi dense slam"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"
    - "informed rrt optimal sampling based path planning focused via direct sampling of an admissible ellipsoidal heuristic"

Connected through hyperedge HE-3 (induced by source paper "generating custom code for efficient query execution on heterogeneous processors"):
  with members:
    - "revisiting co processing for hash joins on the coupled cpu gpu architecture"

Connected through hyperedge HE-4 (induced by source paper "high level gpu programming in julia"):
  with members:
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-5 (induced by source paper "project beehive a hardware software co designed stack for runtime and architectural research"):
  with members:
    - "introducing slambench a performance and accuracy benchmarking methodology for slam"

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)