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: "on the practically interesting instances of maxcut"
  Abstract: The complexity of a computational problem is traditionally quantified based on the hardness of its worst case. This approach has many advantages and has led to a deep and beautiful theory. However, from the practical perspective, this leaves much to be desired. In application areas, practically interesting instances very often occupy just a tiny part of an algorithm's space of instances, and the vast majority of instances are simply irrelevant. Addressing these issues is a major challenge for theoretical computer science which may make theory more relevant to the practice of computer science. #R##N#Following Bilu and Linial, we apply this perspective to MAXCUT, viewed as a clustering problem. Using a variety of techniques, we investigate practically interesting instances of this problem. Specifically, we show how to solve in polynomial time distinguished, metric, expanding and dense instances of MAXCUT under mild stability assumptions. In particular, $(1+\epsilon)$-stability (which is optimal) suffices for metric and dense MAXCUT. We also show how to solve in polynomial time $\Omega(\sqrt{n})$-stable instances of MAXCUT, substantially improving the best previously known result.

Connected through hyperedge HE-1 (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-2 (induced by source paper "exact algorithms and lower bounds for stable instances of euclidean k means"):
  with members:
    - "clustering stable instances of euclidean k means"
    - "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"
    - "bilu linial stable instances of max cut and minimum multiway cut"

Connected through hyperedge HE-3 (induced by source paper "one size fits all effectiveness of local search on structured data"):
  with members:
    - "the power of local search for clustering"
    - "tight analysis of a multiple swap heuristic for budgeted red blue median"
    - "local search yields a ptas for k means in doubling metrics"
    - "on variants of k means clustering"
    - "k center clustering under perturbation resilience"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-4 (induced by source paper "hierarchical clustering objective functions and algorithms"):
  with members:
    - "hierarchical clustering better than average linkage"
    - "hierarchical clustering with structural constraints"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "a uniqueness theorem for clustering"
    - "improved spectral norm bounds for clustering"
    - "efficient active algorithms for hierarchical clustering"

Connected through hyperedge HE-5 (induced by source paper "stability and recovery for independence systems"):
  with members:
    - "k center clustering under perturbation resilience"
    - "computational feasibility of clustering under clusterability assumptions"
    - "the unreasonable success of local search geometric optimization"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "improved approximation for 3 dimensional matching via bounded pathwidth local search"
    - "large neighborhood local search for the maximum set packing problem"
    - "clustering is difficult only when it does not matter"
    - "nash equilibria in perturbation resilient games"

Connected through hyperedge HE-6 (induced by source paper "metric perturbation resilience"):
  with members:
    - "symmetric and asymmetric k center clustering under stability"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"

Connected through hyperedge HE-7 (induced by source paper "k center clustering under perturbation resilience"):
  with members:
    - "improved and simplified inapproximability for k means"
    - "a bi criteria approximation algorithm for k means"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bilu linial stable instances of max cut and minimum multiway cut"
    - "decompositions of triangle dense graphs"
    - "improved spectral norm bounds for clustering"
    - "beyond worst case analysis in private singular vector computation"
    - "learning topic models going beyond svd"

Connected through hyperedge HE-8 (induced by source paper "computational feasibility of clustering under clusterability assumptions"):
  with members:
    - "data stability in clustering a closer look"
    - "center based clustering under perturbation stability"
    - "are stable instances easy"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

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.CC(Computational Complexity)