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: "dominant resource fairness in cloud computing systems with heterogeneous servers"
  Abstract: We study the multi-resource allocation problem in cloud computing systems where the resource pool is constructed from a large number of heterogeneous servers, representing different points in the configuration space of resources such as processing, memory, and storage. We design a multi-resource allocation mechanism, called DRFH, that generalizes the notion of Dominant Resource Fairness (DRF) from a single server to multiple heterogeneous servers. DRFH provides a number of highly desirable properties. With DRFH, no user prefers the allocation of another user; no one can improve its allocation without decreasing that of the others; and more importantly, no user has an incentive to lie about its resource demand. As a direct application, we design a simple heuristic that implements DRFH in real-world systems. Large-scale simulations driven by Google cluster traces show that DRFH significantly outperforms the traditional slot-based scheduler, leading to much higher resource utilization with substantially shorter job completion times.

Connected through hyperedge HE-1 (induced by source paper "differentially private combinatorial cloud auction"):
  with members:
    - "differentially private double spectrum auction with approximate social welfare maximization"
    - "auction mechanisms in cloud fog computing resource allocation for public blockchain networks"
    - "differentially private combinatorial optimization"

Connected through hyperedge HE-2 (induced by source paper "dynamic weighted fairness with minimal disruptions"):
  with members:
    - "dynamic fair division problem with general valuations"
    - "competitive algorithms from competitive equilibria non clairvoyant scheduling under polyhedral constraints"

Connected through hyperedge HE-3 (induced by source paper "reinforcement learning with non markovian rewards"):
  with members:
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "proximal policy optimization algorithms"
    - "learning to optimize"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "exploring the fairness and resource distribution in an apache mesos environment"):
  with members:
    - "scylla a mesos framework for container based mpi jobs"

Connected through hyperedge HE-5 (induced by source paper "multi resource fair allocation with bounded number of tasks in cloud computing systems"):
  with members:
    - "enhanced cluster computing performance through proportional fairness"
    - "multi resource fairness objectives algorithms and performance"
    - "fair allocation without trade"
    - "no justified complaints on fair sharing of multiple resources"

Connected through hyperedge HE-6 (induced by source paper "enhanced cluster computing performance through proportional fairness"):
  with members:
    - "fair allocation without trade"
    - "no justified complaints on fair sharing of multiple resources"

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)