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: "when two choices are not enough balancing at scale in distributed stream processing"
  Abstract: Carefully balancing load in distributed stream processing systems has a fundamental impact on execution latency and throughput. Load balancing is challenging because real-world workloads are skewed: some tuples in the stream are associated to keys which are significantly more frequent than others. Skew is remarkably more problematic in large deployments: more workers implies fewer keys per worker, so it becomes harder to "average out" the cost of hot keys with cold keys. #R##N#We propose a novel load balancing technique that uses a heaving hitter algorithm to efficiently identify the hottest keys in the stream. These hot keys are assigned to $d \geq 2$ choices to ensure a balanced load, where $d$ is tuned automatically to minimize the memory and computation cost of operator replication. The technique works online and does not require the use of routing tables. Our extensive evaluation shows that our technique can balance real-world workloads on large deployments, and improve throughput and latency by $\mathbf{150\%}$ and $\mathbf{60\%}$ respectively over the previous state-of-the-art when deployed on Apache Storm.

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"
    - "the power of both choices practical load balancing for distributed stream processing engines"

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"
    - "the power of both choices practical load balancing for distributed stream processing engines"

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"
    - "the power of both choices practical load balancing for distributed stream processing engines"
    - "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"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-5 (induced by source paper "efficient time evolving stream processing at scale"):
  with members:
    - "task allocation for distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"
    - "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"
    - "the power of both choices practical load balancing for distributed stream processing engines"

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"
    - "the power of both choices practical load balancing for distributed stream processing engines"

Connected through hyperedge HE-8 (induced by source paper "top k densest subgraphs in sliding window graph streams"):
  with members:
    - "a fast order based approach for core maintenance"
    - "densest subgraph in dynamic graph streams"
    - "density friendly graph decomposition"
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "densest subgraph in streaming and mapreduce"
    - "defining and evaluating network communities based on ground truth"

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)