human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "henge intent driven multi tenant stream processing"):
  Abstract: We present Henge, a system to support intent-based multi-tenancy in modern stream processing applications. Henge supports multi-tenancy as a first-class citizen: everyone inside an organization can now submit their stream processing jobs to a single, shared, consolidated cluster. Additionally, Henge allows each tenant (job) to specify its own intents (i.e., requirements) as a Service Level Objective (SLO) that captures latency and/or throughput. In a multi-tenant cluster, the Henge scheduler adapts continually to meet jobs' SLOs in spite of limited cluster resources, and under dynamic input workloads. SLOs are soft and are based on utility functions. Henge continually tracks SLO satisfaction, and when jobs miss their SLOs, it wisely navigates the state space to perform resource allocations in real time, maximizing total system utility achieved by all jobs in the system. Henge is integrated in Apache Storm and we present experimental results using both production topologies and real datasets.

Includes members:
  Member "slaq quality driven scheduling for distributed machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypersched dynamic resource reallocation for model development on a deadline")
  Member "r storm resource aware scheduling in storm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a scheduling algorithm to maximize storm throughput in heterogeneous cluster")
  Member "drs dynamic resource scheduling for real time analytics over fast streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks")
  Member "when two choices are not enough balancing at scale in distributed stream processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "swarm adaptive load balancing in distributed streaming systems for big spatial data")
  Member "the power of both choices practical load balancing for distributed stream processing engines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "swarm adaptive load balancing in distributed streaming systems for big spatial data")

Question: Please tell me which class the source paper 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)