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: "streaming graph partitioning in the planted partition model"
  Abstract: The sheer increase in the size of graph data has created a lot of interest into developing efficient distributed graph processing frameworks. Popular existing frameworks such as Graphlab and Pregel rely on balanced graph partitioning in order to minimize communication and achieve work balance. #R##N#In this work we contribute to the recent research line of streaming graph partitioning \cite{stantonstreaming,stanton,fennel} which computes an approximately balanced $k$-partitioning of the vertex set of a graph using a single pass over the graph stream using degree-based criteria. This graph partitioning framework is well tailored to processing large-scale and dynamic graphs. In this work we introduce the use of higher length walks for streaming graph partitioning and show that their use incurs a minor computational cost which can significantly improve the quality of the graph partition. We perform an average case analysis of our algorithm using the planted partition model \cite{condon2001algorithms,mcsherry2001spectral}. We complement the recent results of Stanton \cite{stantonstreaming} by showing that our proposed method recovers the true partition with high probability even when the gap of the model tends to zero as the size of the graph grows. Furthermore, among the wide number of choices for the length of the walks we show that the proposed length is optimal. Finally, we conduct experiments which verify the value of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "novel dense subgraph discovery primitives risk aversion and exclusion queries"):
  with members:
    - "robust densest subgraph discovery"
    - "on finding dense common subgraphs"
    - "twawler a lightweight twitter crawler"
    - "risk averse matchings over uncertain graph databases"
    - "core decomposition in multilayer networks theory algorithms and applications"
    - "robust influence maximization"
    - "robust influence maximization"
    - "best friends forever bff finding lasting dense subgraphs"

Connected through hyperedge HE-2 (induced by source paper "flowless extracting densest subgraphs without flow computations"):
  with members:
    - "near optimal fully dynamic densest subgraph"
    - "twittermancer predicting interactions on twitter accurately"
    - "novel dense subgraph discovery primitives risk aversion and exclusion queries"
    - "parallel and streaming algorithms for k core decomposition"
    - "the densest subgraph problem with a convex concave size function"
    - "densest subgraph in dynamic graph streams"
    - "density friendly graph decomposition"
    - "applications of uniform sampling densest subgraph and beyond"

Connected through hyperedge HE-3 (induced by source paper "optimal learning of joint alignments with a faulty oracle"):
  with members:
    - "joint alignment from pairwise differences with a noisy oracle"
    - "predicting positive and negative links with noisy queries theory practice"
    - "the projected power method an efficient algorithm for joint alignment from pairwise differences"
    - "near optimal joint object matching via convex relaxation"
    - "exact and stable recovery of rotations for robust synchronization"

Connected through hyperedge HE-4 (induced by source paper "window based streaming graph partitioning algorithm"):
  with members:
    - "s powergraph streaming graph partitioning for natural graphs by vertex cut"

Connected through hyperedge HE-5 (induced by source paper "block models and personalized pagerank"):
  with members:
    - "community detection in networks with unequal groups"
    - "find your place simple distributed algorithms for community detection"
    - "heat kernel based community detection"
    - "pagerank beyond the web"
    - "exact recovery in the stochastic block model"
    - "spectral redemption in clustering sparse networks"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "community detection thresholds and the weak ramanujan property"

Connected through hyperedge HE-6 (induced by source paper "predicting positive and negative links with noisy queries theory practice"):
  with members:
    - "scalable motif aware graph clustering"
    - "clustering via crowdsourcing"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "a new correlation clustering method for cancer mutation analysis"
    - "correlation clustering with noisy partial information"
    - "analysis of boolean functions"
    - "mathematical and algorithmic analysis of network and biological data"
    - "exact recovery in the stochastic block 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.DS(Data Structures and Algorithms)