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: "approximate correlation clustering using same cluster queries"
  Abstract: Ashtiani et al. (NIPS 2016) introduced a semi-supervised framework for clustering (SSAC) where a learner is allowed to make same-cluster queries. More specifically, in their model, there is a query oracle that answers queries of the form given any two vertices, do they belong to the same optimal cluster?. Ashtiani et al. showed the usefulness of such a query framework by giving a polynomial time algorithm for the k-means clustering problem where the input dataset satisfies some separation condition. Ailon et al. extended the above work to the approximation setting by giving an efficient (1+\eps)-approximation algorithm for k-means for any small \eps > 0 and any dataset within the SSAC framework. In this work, we extend this line of study to the correlation clustering problem. Correlation clustering is a graph clustering problem where pairwise similarity (or dissimilarity) information is given for every pair of vertices and the objective is to partition the vertices into clusters that minimise the disagreement (or maximises agreement) with the pairwise information given as input. These problems are popularly known as MinDisAgree and MaxAgree problems, and MinDisAgree[k] and MaxAgree[k] are versions of these problems where the number of optimal clusters is at most k. There exist Polynomial Time Approximation Schemes (PTAS) for MinDisAgree[k] and MaxAgree[k] where the approximation guarantee is (1+\eps) for any small \eps and the running time is polynomial in the input parameters but exponential in k and 1/\eps. We obtain an (1+\eps)-approximation algorithm for any small \eps with running time that is polynomial in the input parameters and also in k and 1/\eps. We also give non-trivial upper and lower bounds on the number of same-cluster queries, the lower bound being based on the Exponential Time Hypothesis (ETH).

Connected through hyperedge HE-1 (induced by source paper "robust clustering oracle and local reconstructor of cluster structure of graphs"):
  with members:
    - "testing graph clusterability algorithms and lower bounds"
    - "approximate clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "learning communities in the presence of errors"
    - "testing cluster structure of graphs"
    - "how robust are reconstruction thresholds for community detection"
    - "correlation clustering with noisy partial information"
    - "partitioning well clustered graphs spectral clustering works"

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"
    - "community recovery in graphs with locality"
    - "a survey of signed network mining in social media"
    - "on the complexity of learning with kernels"
    - "local correlation clustering"
    - "community detection thresholds and the weak ramanujan property"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-3 (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 clustering with same cluster queries"
    - "clustering with same cluster queries"
    - "fault tolerant entity resolution with the crowd"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-4 (induced by source paper "semi supervised clustering for de duplication"):
  with members:
    - "clustering with same cluster queries"

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)