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: "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
  Abstract: We give new rounding schemes for the standard linear programming relaxation of the correlation clustering problem, achieving approximation factors almost matching the integrality gaps: #R##N#- For complete graphs our appoximation is $2.06 - \varepsilon$ for a fixed constant $\varepsilon$, which almost matches the previously known integrality gap of $2$. #R##N#- For complete $k$-partite graphs our approximation is $3$. We also show a matching integrality gap. #R##N#- For complete graphs with edge weights satisfying triangle inequalities and probability constraints, our approximation is $1.5$, and we show an integrality gap of $1.2$. #R##N#Our results improve a long line of work on approximation algorithms for correlation clustering in complete graphs, previously culminating in a ratio of $2.5$ for the complete case by Ailon, Charikar and Newman (JACM'08). In the weighted complete case satisfying triangle inequalities and probability constraints, the same authors give a $2$-approximation; for the bipartite case, Ailon, Avigdor-Elgrabli, Liberty and van Zuylen give a $4$-approximation (SICOMP'12).

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (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"
    - "local correlation clustering"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-3 (induced by source paper "learning resolution parameters for graph clustering"):
  with members:
    - "a parallel projection method for metric constrained optimization"
    - "correlation clustering generalized"
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"
    - "an optimization approach to locally biased graph algorithms"
    - "higher order organization of complex networks"

Connected through hyperedge HE-4 (induced by source paper "a parallel projection method for metric constrained optimization"):
  with members:
    - "a projection method for metric constrained optimization"
    - "correlation clustering and biclustering with locally bounded errors"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"

Connected through hyperedge HE-5 (induced by source paper "min max correlation clustering via multicut"):
  with members:
    - "local guarantees in graph cuts and clustering"
    - "instancecut from edges to instances with multicut"
    - "correlation clustering and biclustering with locally bounded errors"

Connected through hyperedge HE-6 (induced by source paper "correlation clustering in data streams"):
  with members:
    - "access to data and number of iterations dual primal algorithms for maximum matching under resource constraints"
    - "a note on no need to choose how to get both a ptas and sublinear query complexity"
    - "large scale correlation clustering optimization"
    - "linear programming in the semi streaming model with application to the maximum matching problem"
    - "ams without 4 wise independence on product domains"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-7 (induced by source paper "a projection method for metric constrained optimization"):
  with members:
    - "network clustering via maximizing modularity approximation algorithms and theoretical limits"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-8 (induced by source paper "motif clustering and overlapping clustering for social network analysis"):
  with members:
    - "higher order organization of complex networks"
    - "parallel correlation clustering on big graphs"
    - "spectral detection on sparse hypergraphs"
    - "a tutorial on spectral clustering"

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)