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: "correlation clustering with a fixed number of clusters"
  Abstract: We continue the investigation of problems concerning correlation clustering or clustering with qualitative information, which is a clustering formulation that has been studied recently. The basic setup here is that we are given as input a complete graph on n nodes (which correspond to nodes to be clustered) whose edges are labeled + (for similar pairs of items) and - (for dissimilar pairs of items). Thus we have only as input qualitative information on similarity and no quantitative distance measure between items. The quality of a clustering is measured in terms of its number of agreements, which is simply the number of edges it correctly classifies, that is the sum of number of - edges whose endpoints it places in different clusters plus the number of + edges both of whose endpoints it places within the same cluster. #R##N#In this paper, we study the problem of finding clusterings that maximize the number of agreements, and the complementary minimization version where we seek clusterings that minimize the number of disagreements. We focus on the situation when the number of clusters is stipulated to be a small constant k. Our main result is that for every k, there is a polynomial time approximation scheme for both maximizing agreements and minimizing disagreements. (The problems are NP-hard for every k >= 2.) The main technical work is for the minimization version, as the PTAS for maximizing agreements follows along the lines of the property tester for Max k-CUT. #R##N#In contrast, when the number of clusters is not specified, the problem of minimizing disagreements was shown to be APX-hard, even though the maximization version admits a PTAS.

Connected through hyperedge HE-1 (induced by source paper "spectral clustering of signed graphs via matrix power means"):
  with members:
    - "balance in signed networks"
    - "an mbo scheme for clustering and semi supervised clustering of signed networks"
    - "node classification for signed networks using diffuse interface methods"
    - "signed graph convolutional network"
    - "on spectral partitioning of signed graphs"
    - "a modularity based spectral method for simultaneous community and anti community detection"
    - "sne signed network embedding"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"

Connected through hyperedge HE-2 (induced by source paper "signed network structural analysis and applications with a focus on balance theory"):
  with members:
    - "balance and frustration in signed networks"
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "evaluating balance on social networks from their simple cycles"
    - "spectral theory of unsigned and signed graphs applications to graph clustering a survey"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "approximate inference in structured instances with noisy categorical observations"):
  with members:
    - "holodetect few shot learning for error detection"
    - "a formal framework for probabilistic unclean databases"
    - "inference in sparse graphs with pairwise measurements and side information"
    - "holoclean holistic data repairs with probabilistic inference"
    - "harnessing deep neural networks with logic rules"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "bidirectional lstm crf models for sequence tagging"
    - "incremental knowledge base construction using deepdive"

Connected through hyperedge HE-4 (induced by source paper "discovering polarized communities in signed networks"):
  with members:
    - "reducing controversy by connecting opposing views"
    - "signed link analysis in social media networks"
    - "a survey of signed network mining in social media"
    - "controversy and sentiment in online news"
    - "correlation clustering with constrained cluster sizes and extended weights bounds"
    - "influence diffusion dynamics and influence maximization in social networks with friend and foe relationships"
    - "the slashdot zoo mining a social network with negative edges"

Connected through hyperedge HE-5 (induced by source paper "correlation clustering with same cluster queries bounded by optimal cost"):
  with members:
    - "semi supervised algorithms for approximately optimal and accurate clustering"
    - "semi supervised clustering for de duplication"
    - "approximate correlation clustering using same cluster queries"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "fault tolerant entity resolution with the crowd"

Connected through hyperedge HE-6 (induced by source paper "balance and frustration in signed networks"):
  with members:
    - "measuring partial balance in signed networks"
    - "computing the line index of balance using integer programming optimisation"
    - "evaluating balance on social networks from their simple cycles"
    - "a general purpose algorithm for counting simple cycles and simple paths of any length"
    - "an exact method for computing the frustration index in signed networks using binary programming"
    - "applications of structural balance in signed social networks"

Connected through hyperedge HE-7 (induced by source paper "correlation clustering in data streams"):
  with members:
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "approximate correlation clustering using same cluster queries"):
  with members:
    - "approximate clustering with same cluster queries"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "clustering with same cluster queries"
    - "predicting signed edges with o n 1 o 1 log n queries"
    - "efficient clustering with limited distance information"
    - "correlation clustering with noisy partial information"
    - "local algorithms for interactive clustering"
    - "clustering under perturbation resilience"

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)