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: "approximating k median via pseudo approximation"
  Abstract: We present a novel approximation algorithm for $k$-median that achieves an approximation guarantee of #R##N#$1+\sqrt{3}+\epsilon$, improving upon the decade-old ratio of $3+\epsilon$. Our approach is based on two components, each of which, we believe, is of independent interest. #R##N#First, we show that in order to give an $\alpha$-approximation algorithm for $k$-median, it is sufficient to give a \emph{pseudo-approximation algorithm} that finds an $\alpha$-approximate solution by opening $k+O(1)$ facilities. This is a rather surprising result as there exist instances for which opening $k+1$ facilities may lead to a significant smaller cost than if only $k$ facilities were opened. #R##N#Second, we give such a pseudo-approximation algorithm with $\alpha= 1+\sqrt{3}+\epsilon$. Prior to our work, it was not even known whether opening $k + o(k)$ facilities would help improve the approximation ratio.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "individual fairness for k clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "r enyi fair inference"
    - "a survey on bias and fairness in machine learning"
    - "fairness in clustering with multiple sensitive attributes"
    - "a center in your neighborhood fairness in facility location"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"

Connected through hyperedge HE-3 (induced by source paper "dependent randomized rounding for clustering and partition systems with knapsack constraints"):
  with members:
    - "on a generalization of iterated and randomized rounding"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "approximation friendly discrepancy rounding"
    - "matroid and knapsack center problems"
    - "fault tolerant facility location a randomized dependent lp rounding algorithm"

Connected through hyperedge HE-4 (induced by source paper "cache optimization models and algorithms"):
  with members:
    - "learning to cache with no regrets"
    - "introduction to online convex optimization"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "competitive caching with machine learned advice"
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "show me the cache optimizing cache friendly recommendations for sequential content access"
    - "a reinforcement learning approach to proactive caching in wireless networks"

Connected through hyperedge HE-5 (induced by source paper "fair algorithms for clustering"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "constant factor fpt approximation for capacitated k median"
    - "a unified framework for clustering constrained data without locality property"
    - "fair clustering through fairlets"
    - "generalized center problems with outliers"

Connected through hyperedge HE-6 (induced by source paper "tight running time lower bounds for strong inapproximability of maximum k coverage unique set cover and related problems via t wise agreement testing theorem"):
  with members:
    - "a simple gap producing reduction for the parameterized set cover problem"
    - "parameterized intractability of even set and shortest vector problem"
    - "parameterized inapproximability of exact cover and nearest codeword"
    - "tight fpt approximations for k median and k means"
    - "agreement testing theorems on layered set systems"
    - "towards a general direct product testing theorem"
    - "gap s eth hardness of svp"
    - "eth hardness of approximating 2 csps and directed steiner network"

Connected through hyperedge HE-7 (induced by source paper "a practical framework for solving center based clustering with outliers"):
  with members:
    - "beyond worst case analysis"
    - "solving k center clustering with outliers in mapreduce and streaming almost as accurately as sequentially"
    - "varepsilon coresets for clustering with outliers in doubling metrics"
    - "distributed partial clustering"
    - "approximation schemes for clustering with outliers"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "the non uniform k center problem"
    - "optimal time bounds for approximate clustering"

Connected through hyperedge HE-8 (induced by source paper "reconciliation k median clustering with non polarized representatives"):
  with members:
    - "constant factor approximation for ordered k median"
    - "minimizing polarization and disagreement in social networks"
    - "balancing information exposure in social networks"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "reducing controversy by connecting opposing views"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "bi factor approximation algorithms for hard capacitated k median problems"

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)