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: "local correlation clustering"
  Abstract: Correlation clustering is perhaps the most natural formulation of clustering. Given $n$ objects and a pairwise similarity measure, the goal is to cluster the objects so that, to the best possible extent, similar objects are put in the same cluster and dissimilar objects are put in different clusters. Despite its theoretical appeal, the practical relevance of correlation clustering still remains largely unexplored, mainly due to the fact that correlation clustering requires the $\Theta(n^2)$ pairwise similarities as input. #R##N#In this paper we initiate the investigation into \emph{local} algorithms for correlation clustering. In \emph{local correlation clustering} we are given the identifier of a single object and we want to return the cluster to which it belongs in some globally consistent near-optimal clustering, using a small number of similarity queries. Local algorithms for correlation clustering open the door to \emph{sublinear-time} algorithms, which are particularly useful when the similarity between items is costly to compute, as it is often the case in many practical application domains. They also imply $(i)$ distributed and streaming clustering algorithms, $(ii)$ constant-time estimators and testers for cluster edit distance, and $(iii)$ property-preserving parallel reconstruction algorithms for clusterability. #R##N#Specifically, we devise a local clustering algorithm attaining a $(3, \varepsilon)$-approximation in time $O(1/\varepsilon^2)$ independently of the dataset size. An explicit approximate clustering for all objects can be produced in time $O(n/\varepsilon)$ (which is provably optimal). We also provide a fully additive $(1,\varepsilon)$-approximation with local query complexity $poly(1/\varepsilon)$ and time complexity $2^{poly(1/\varepsilon)}$. The latter yields the fastest polynomial-time approximation scheme for correlation clustering known to date.

Connected through hyperedge HE-1 (induced by source paper "query efficient correlation clustering"):
  with members:
    - "correlation clustering with adaptive similarity queries"
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "revisiting semi supervised learning with graph embeddings"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-2 (induced by source paper "correlation clustering with adaptive similarity queries"):
  with members:
    - "correlation clustering with same cluster queries bounded by optimal cost"
    - "predicting positive and negative links with noisy queries theory practice"
    - "approximate correlation clustering using same cluster queries"
    - "community recovery in graphs with locality"
    - "a survey of signed network mining in social media"
    - "on the complexity of learning with kernels"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "regular partitions and their use in structural pattern recognition"):
  with members:
    - "separating structure from noise in large graphs using the regularity lemma"
    - "analysis of large sparse graphs using regular decomposition of graph distance matrices"
    - "network summarization with preserved spectral properties"
    - "regular decomposition an information and graph theoretic approach to stochastic block models"
    - "on the interplay between strong regularity and graph densification"
    - "fast edge detection using structured forests"

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)