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: "vht vertical hoeffding tree"
  Abstract: IoT Big Data requires new machine learning methods able to scale to large size of data arriving at high speed. Decision trees are popular machine learning models since they are very effective, yet easy to interpret and visualize. In the literature, we can find distributed algorithms for learning decision trees, and also streaming algorithms, but not algorithms that combine both features. In this paper we present the Vertical Hoeffding Tree (VHT), the first distributed streaming algorithm for learning decision trees. It features a novel way of distributing decision trees via vertical parallelism. The algorithm is implemented on top of Apache SAMOA, a platform for mining distributed data streams, and thus able to run on real-world clusters. We run several experiments to study the accuracy and throughput performance of our new VHT algorithm, as well as its ability to scale while keeping its superior performance with respect to non-distributed decision trees.

Connected through hyperedge HE-1 (induced by source paper "large scale learning from data streams with apache samoa"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "when two choices are not enough balancing at scale in distributed stream processing"
    - "the power of both choices practical load balancing for distributed stream processing engines"
    - "scalable online betweenness centrality in evolving graphs"

Connected through hyperedge HE-2 (induced by source paper "online machine learning in big data streams"):
  with members:
    - "computing web scale topic models using an asynchronous parameter server"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "clipper a low latency online prediction serving system"
    - "xgboost a scalable tree boosting system"
    - "general factorization framework for context aware recommendations"
    - "evaluation of recommender systems in streaming environments"
    - "optimal and adaptive algorithms for online boosting"
    - "massively parallel methods for deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "hoeffding trees with nmin adaptation"):
  with members:
    - "designing energy efficient convolutional neural networks using energy aware pruning"

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)