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: "analysis and modeling of social influence in high performance computing workloads"
  Abstract: Social influence among users (e.g., collaboration on a project) creates bursty behavior in the underlying high performance computing (HPC) workloads. Using representative HPC and cluster workload logs, this paper identifies, analyzes, and quantifies the level of social influence across HPC users. We show the existence of a social graph that is characterized by a pattern of dominant users and followers. This pattern also follows a power-law distribution, which is consistent with those observed in mainstream social networks. Given its potential impact on HPC workloads prediction and scheduling, we propose a fast-converging, computationally-efficient online learning algorithm for identifying social groups. Extensive evaluation shows that our online algorithm can (1) quickly identify the social relationships by using a small portion of incoming jobs and (2) can efficiently track group evolution over time.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks for failure prediction"):
  with members:
    - "a cost sensitive deep belief network for imbalanced classification"
    - "minimal support vector machine"
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "regularized singular value decomposition and application to recommender system"
    - "camera style adaptation for person re identification"
    - "controllable generative adversarial network"
    - "a bayesian data augmentation approach for learning deep models"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "manufacturing dispatching using reinforcement and transfer learning"):
  with members:
    - "minimal support vector machine"
    - "regularized singular value decomposition and application to recommender system"
    - "cross domain transfer in reinforcement learning using target apprentice"
    - "a harmonic mean linear discriminant analysis for robust image classification"
    - "accelerating deep learning with shrinkage and recall"
    - "kernel alignment inspired linear discriminant analysis"

Connected through hyperedge HE-3 (induced by source paper "regularized singular value decomposition and application to recommender system"):
  with members:
    - "minimal support vector machine"
    - "a harmonic mean linear discriminant analysis for robust image classification"
    - "accelerating deep learning with shrinkage and recall"
    - "kernel alignment inspired linear discriminant analysis"

Connected through hyperedge HE-4 (induced by source paper "minimal support vector machine"):
  with members:
    - "accelerating deep learning with shrinkage and recall"
    - "kernel alignment inspired linear discriminant analysis"
    - "a closed form solution to multi view low rank regression"

Connected through hyperedge HE-5 (induced by source paper "accelerating deep learning with shrinkage and recall"):
  with members:
    - "kernel alignment inspired linear discriminant analysis"
    - "a closed form solution to multi view low rank regression"
    - "matconvnet convolutional neural networks for matlab"
    - "fast support vector machines using parallel adaptive shrinking on distributed systems"

Connected through hyperedge HE-6 (induced by source paper "a harmonic mean linear discriminant analysis for robust image classification"):
  with members:
    - "accelerating deep learning with shrinkage and recall"
    - "kernel alignment inspired linear discriminant analysis"
    - "a closed form solution to multi view low rank regression"

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)