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: "using meta heuristics and machine learning for software optimization of parallel computing systems a systematic literature review"
  Abstract: While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectu ...

Connected through hyperedge HE-1 (induced by source paper "using structured input and modularity for improved learning"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "compositional attention networks for machine reasoning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "dynamic routing between capsules"
    - "learning to infer graphics programs from hand drawn images"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to compose neural networks for question answering"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-2 (induced by source paper "metareasoning in modular software systems on the fly configuration using reinforcement learning with rich contextual representations"):
  with members:
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep reinforcement learning a brief survey"
    - "device placement optimization with reinforcement learning"
    - "deep reinforcement learning an overview"
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "deep residual learning for image recognition"
    - "perception attention and resources a decision theoretic approach to graphics rendering"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "performance modelling of deep learning on intel many integrated core architectures"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "performance modeling and evaluation of distributed deep learning frameworks on gpus"
    - "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption"
    - "znn a fast and scalable algorithm for training 3d convolutional networks on multi core and many core shared memory machines"
    - "deep learning in neural networks"
    - "sequence to sequence learning with neural networks"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "hstream a directive based language extension for heterogeneous stream computing"):
  with members:
    - "benchmarking opencl openacc openmp and cuda programming productivity performance and energy consumption"
    - "analyzing large scale dna sequences on multi core architectures"

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)