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: "unifying sparsest cut cluster deletion and modularity clustering objectives with correlation clustering"
  Abstract: We present and analyze a new framework for graph clustering based on a specially weighted version of correlation clustering, that unifies several existing objectives and satisfies a number of attractive theoretical properties. Our framework, which we call LambdaCC, relies on a single resolution parameter $\lambda$, which implicitly controls both the edge density and sparsest cut of all output clusters. We prove that our new clustering objective interpolates between the cluster deletion problem and the minimum sparsest cut problem as we vary $\lambda$, and is also closely related to the well-studied maximum modularity objective. We provide several algorithms for optimizing our new objective, including a 5-approximation for the case where $\lambda \geq 1/2$, and also the first constant factor approximation algorithm for the NP-hard cluster deletion problem. We demonstrate the effectiveness of our framework and algorithms in finding communities in several real-world networks.

Connected through hyperedge HE-1 (induced by source paper "stochastic block models are a discrete surface tension"):
  with members:
    - "community detection in networks via nonlinear modularity eigenvectors"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "unsupervised classification in hyperspectral imagery with nonlocal total variation and primal dual hybrid gradient algorithm"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "efficient method for estimating the number of communities in a network"
    - "block models and personalized pagerank"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-2 (induced by source paper "correlation clustering generalized"):
  with members:
    - "higher order organization of complex networks"
    - "motif clustering and overlapping clustering for social network analysis"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"

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)