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: "the power of both choices practical load balancing for distributed stream processing engines"
  Abstract: We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation. In so doing, it achieves better load balancing than key grouping while being more scalable than shuffle grouping. We test PKG on several large datasets, both real-world and synthetic. Compared to standard hashing, PKG reduces the load imbalance by up to several orders of magnitude, and often achieves nearly-perfect load balance. This result translates into an improvement of up to 60% in throughput and up to 45% in latency when deployed on a real Storm cluster.

Connected through hyperedge HE-1 (induced by source paper "swarm adaptive load balancing in distributed streaming systems for big spatial data"):
  with members:
    - "distributed publish subscribe query processing on the spatio textual data stream"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive survey on parallelization and elasticity in stream processing"):
  with members:
    - "p4cep towards in network complex event processing"
    - "toward reliable and rapid elasticity for streaming dataflows on clouds"
    - "minimizing communication overhead in window based parallel complex event processing"
    - "event forecasting with pattern markov chains"
    - "distributed data stream processing and edge computing a survey on resource elasticity and future directions"
    - "spectre supporting consumption policies in window based parallel complex event processing"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-3 (induced by source paper "large scale learning from data streams with apache samoa"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "vht vertical hoeffding tree"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-4 (induced by source paper "henge intent driven multi tenant stream processing"):
  with members:
    - "slaq quality driven scheduling for distributed machine learning"
    - "r storm resource aware scheduling in storm"
    - "drs dynamic resource scheduling for real time analytics over fast streams"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-5 (induced by source paper "efficient time evolving stream processing at scale"):
  with members:
    - "task allocation for distributed stream processing"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-6 (induced by source paper "a survey of state management in big data processing systems"):
  with members:
    - "elasticutor rapid elasticity for realtime stateful stream processing"
    - "incremental query processing on big data streams"
    - "integrative dynamic reconfiguration in a parallel stream processing engine"
    - "frogwild fast pagerank approximations on graph engines"
    - "incremental computation with names"
    - "s store streaming meets transaction processing"
    - "tolerating correlated failures in massively parallel stream processing engines"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-7 (induced by source paper "load balancing for skewed streams on heterogeneous clusters"):
  with members:
    - "consistent hashing with bounded loads"
    - "partial key grouping load balanced partitioning of distributed streams"
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "when two choices are not enough balancing at scale in distributed stream processing"

Connected through hyperedge HE-8 (induced by source paper "a formal semantics for data analytics pipelines"):
  with members:
    - "a comparison of big data frameworks on a layered dataflow model"

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)