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: "clustering with same cluster queries"
  Abstract: We propose a framework for Semi-Supervised Active Clustering framework (SSAC), where the learner is allowed to interact with a domain expert, asking whether two given instances belong to the same cluster or not. We study the query and computational complexity of clustering in this framework. We consider a setting where the expert conforms to a center-based clustering with a notion of margin. We show that there is a trade off between computational complexity and query complexity; We prove that for the case of $k$-means clustering (i.e., when the expert conforms to a solution of $k$-means), having access to relatively few such queries allows efficient solutions to otherwise NP hard problems. #R##N#In particular, we provide a probabilistic polynomial-time (BPP) algorithm for clustering in this setting that asks $O\big(k^2\log k + k\log n)$ same-cluster queries and runs with time complexity $O\big(kn\log n)$ (where $k$ is the number of clusters and $n$ is the number of instances). The algorithm succeeds with high probability for data satisfying margin conditions under which, without queries, we show that the problem is NP hard. We also prove a lower bound on the number of queries needed to have a computationally efficient clustering algorithm in this setting.

Connected through hyperedge HE-1 (induced by source paper "explainable k means and k medians clustering"):
  with members:
    - "individual fairness for k clustering"
    - "coresets for clustering with fairness constraints"
    - "same cluster querying for overlapping clusters"
    - "online k means clustering"
    - "fair algorithms for clustering"
    - "unexpected effects of online k means clustering"
    - "sequential no substitution k median clustering"
    - "scalable fair clustering"

Connected through hyperedge HE-2 (induced by source paper "diameter based interactive structure discovery"):
  with members:
    - "diameter based active learning"
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "equality of opportunity in supervised learning"
    - "efficient learning of linear separators under bounded noise"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "local algorithms for interactive clustering"
    - "robust interactive learning"

Connected through hyperedge HE-3 (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 correlation clustering using same cluster queries"
    - "approximate 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-4 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "generalized binary search for split neighborly problems"
    - "diameter based active learning"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "local algorithms for interactive clustering"
    - "efficient active learning of halfspaces an aggressive approach"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-5 (induced by source paper "same cluster querying for overlapping clusters"):
  with members:
    - "community recovery in hypergraphs"
    - "predicting positive and negative links with noisy queries theory practice"
    - "a theoretical analysis of first heuristics of crowdsourced entity resolution"
    - "approximate clustering with same cluster queries"
    - "leveraging transitive relations for crowdsourced joins"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (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"
    - "fault tolerant entity resolution with the crowd"
    - "correlation clustering with a fixed number of clusters"

Connected through hyperedge HE-7 (induced by source paper "diameter based interactive structure search"):
  with members:
    - "a general framework for robust interactive learning"
    - "interactive bayesian hierarchical clustering"
    - "efficient learning of linear separators under bounded noise"
    - "local algorithms for interactive clustering"

Connected through hyperedge HE-8 (induced by source paper "hs 2 active learning over hypergraphs"):
  with members:
    - "on the minimax misclassification ratio of hypergraph community detection"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "quadratic decomposable submodular function minimization"
    - "predicting positive and negative links with noisy queries theory practice"
    - "inhomogoenous hypergraph clustering with applications"
    - "higher order organization of complex networks"
    - "active learning on trees and graphs"
    - "active semi supervised learning using submodular functions"

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.LG(Machine Learning)