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 under perturbation resilience"
  Abstract: Motivated by the fact that distances between data points in many real-world clustering instances are often based on heuristic measures, Bilu and Linial~\cite{BL} proposed analyzing objective based clustering problems under the assumption that the optimum clustering to the objective is preserved under small multiplicative perturbations to distances between points. The hope is that by exploiting the structure in such instances, one can overcome worst case hardness results. #R##N#In this paper, we provide several strong results within this framework. For center-based objectives, we present an algorithm that can optimally cluster instances resilient to perturbations of factor $(1 + \sqrt{2})$, solving an open problem of Awasthi et al.~\cite{ABS10}. For $k$-median, a center-based objective of special interest, we additionally give algorithms for a more relaxed assumption in which we allow the optimal solution to change in a small $\epsilon$ fraction of the points after perturbation. We give the first bounds known for $k$-median under this more realistic and more general assumption. We also provide positive results for min-sum clustering which is a generally much harder objective than center-based objectives. Our algorithms are based on new linkage criteria that may be of independent interest. #R##N#Additionally, we give sublinear-time algorithms, showing algorithms that can return an implicit clustering from only access to a small random sample.

Connected through hyperedge HE-1 (induced by source paper "analysis of ward s method"):
  with members:
    - "metric perturbation resilience"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "a cost function for similarity based hierarchical clustering"
    - "improved and simplified inapproximability for k means"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the hardness of approximation of euclidean k means"
    - "analysis of agglomerative clustering"

Connected through hyperedge HE-2 (induced by source paper "semi bandit optimization in the dispersed setting"):
  with members:
    - "data driven clustering via parameterized lloyd s families"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "k center clustering under perturbation resilience"
    - "a pac approach to application specific algorithm selection"

Connected through hyperedge HE-3 (induced by source paper "minimum enclosing ball revisited stability sub linear time algorithms and extension"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "distributed partial clustering"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "the non uniform k center problem"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"

Connected through hyperedge HE-4 (induced by source paper "minimum enclosing ball revisited stability and sub linear time algorithms"):
  with members:
    - "beyond worst case analysis"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "coresets and sketches"
    - "locating a small cluster privately"
    - "k center clustering under perturbation resilience"
    - "optimization algorithms for faster computational geometry"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "clustering with spectral norm and the k means algorithm"

Connected through hyperedge HE-5 (induced by source paper "learning to link"):
  with members:
    - "how much data is sufficient to learn high performing algorithms"
    - "data driven clustering via parameterized lloyd s families"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "local algorithms for interactive clustering"
    - "center based clustering under perturbation stability"

Connected through hyperedge HE-6 (induced by source paper "on euclidean k means clustering with alpha center proximity"):
  with members:
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "approximation schemes for clustering with outliers"
    - "clustering stable instances of euclidean k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "k center clustering under perturbation resilience"
    - "computational feasibility of clustering under clusterability assumptions"

Connected through hyperedge HE-7 (induced by source paper "bilu linial stability certified algorithms and the independent set problem"):
  with members:
    - "on euclidean k means clustering with alpha center proximity"
    - "exact algorithms and lower bounds for stable instances of euclidean k means"
    - "perturbation resilient clustering for k center and related problems via lp relaxations"
    - "stability and recovery for independence systems"
    - "lp based robust algorithms for noisy minor free and bounded treewidth graphs"
    - "k center clustering under perturbation resilience"
    - "on the lov asz theta function for independent sets in sparse graphs"
    - "sum of squares lower bounds for planted clique"

Connected through hyperedge HE-8 (induced by source paper "reverse greedy is bad for k center"):
  with members:
    - "approximation algorithms for minimum norm and ordered optimization problems"
    - "constant factor approximation for ordered k median"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "center based clustering under perturbation stability"
    - "the reverse greedy algorithm for the metric k median problem"

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)