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: "approximation algorithms for minimum norm and ordered optimization problems"
  Abstract: $ $In many optimization problems, a feasible solution induces a multi-dimensional cost vector. For example, in load-balancing a schedule induces a load vector across the machines. In $k$-clustering, opening $k$ facilities induces an assignment cost vector across the clients. In this paper we consider the following minimum norm optimization problem : Given an arbitrary monotone, symmetric norm, find a solution which minimizes the norm of the induced cost-vector. This generalizes many fundamental NP-hard problems. #R##N#We give a general framework to tackle the minimum norm problem, and illustrate its efficacy in the unrelated machine load balancing and $k$-clustering setting. Our concrete results are the following. #R##N#$\bullet$ We give constant factor approximation algorithms for the minimum norm load balancing problem in unrelated machines, and the minimum norm $k$-clustering problem. To our knowledge, our results constitute the first constant-factor approximations for such a general suite of objectives. #R##N#$\bullet$ In load balancing with unrelated machines, we give a $2$-approximation for the problem of finding an assignment minimizing the sum of the largest $\ell$ loads, for any $\ell$. We give a $(2+\varepsilon)$-approximation for the so-called ordered load-balancing problem. #R##N#$\bullet$ For $k$-clustering, we give a $(5+\varepsilon)$-approximation for the ordered $k$-median problem significantly improving the constant factor approximations from Byrka, Sornat, and Spoerhase (STOC 2018) and Chakrabarty and Swamy (ICALP 2018). #R##N#$\bullet$ Our techniques also imply $O(1)$ approximations to the best simultaneous optimization factor for any instance of the unrelated machine load-balancing and the $k$-clustering setting. To our knowledge, these are the first positive simultaneous optimization results in these settings.

Connected through hyperedge HE-1 (induced by source paper "coresets for ordered weighted clustering"):
  with members:
    - "privacy preserving clustering with constraints"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fair clustering through fairlets"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "constant factor approximation for ordered k median"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-2 (induced by source paper "simpler and better algorithms for minimum norm load balancing"):
  with members:
    - "constant factor approximation for ordered k median"
    - "interpolating between k median and k center approximation algorithms for ordered k median"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "on the configuration lp of the restricted assignment problem"

Connected through hyperedge HE-3 (induced by source paper "reverse greedy is bad for k center"):
  with members:
    - "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"
    - "clustering under perturbation resilience"
    - "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.DS(Data Structures and Algorithms)