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: "algorithmic discrepancy beyond partial coloring"
  Abstract: The partial coloring method is one of the most powerful and widely used method in combinatorial discrepancy problems. However, in many cases it leads to sub-optimal bounds as the partial coloring step must be iterated a logarithmic number of times, and the errors can add up in an adversarial way. We give a new and general algorithmic framework that overcomes the limitations of the partial coloring method and can be applied in a black-box manner to various problems. Using this framework, we give new improved bounds and algorithms for several classic problems in discrepancy. In particular, for Tusnady's problem, we give an improved $O(\log^2 n)$ bound for discrepancy of axis-parallel rectangles and more generally an $O_d(\log^dn)$ bound for $d$-dimensional boxes in $\mathbb{R}^d$. Previously, even non-constructively, the best bounds were $O(\log^{2.5} n)$ and $O_d(\log^{d+0.5}n)$ respectively. Similarly, for the Steinitz problem we give the first algorithm that matches the best known non-constructive bounds due to Banaszczyk \cite{Bana12} in the $\ell_\infty$ case, and improves the previous algorithmic bounds substantially in the $\ell_2$ case. Our framework is based upon a substantial generalization of the techniques developed recently in the context of the Koml\'{o}s discrepancy problem [BDG16].

Connected through hyperedge HE-1 (induced by source paper "a spectral approach to network design"):
  with members:
    - "network design for s t effective resistance"
    - "new notions and constructions of sparsification for graphs and hypergraphs"
    - "on a generalization of iterated and randomized rounding"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "graph clustering using effective resistance"

Connected through hyperedge HE-2 (induced by source paper "generalised pattern matching revisited"):
  with members:
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "constructive discrepancy minimization with hereditary l2 guarantees"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-3 (induced by source paper "online vector balancing and geometric discrepancy"):
  with members:
    - "on line balancing of random inputs"
    - "online geometric discrepancy for stochastic arrivals with applications to envy minimization"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "constructive discrepancy minimization for convex sets"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-4 (induced by source paper "online geometric discrepancy for stochastic arrivals with applications to envy minimization"):
  with members:
    - "on line balancing of random inputs"
    - "on the discrepancy of random low degree set systems"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "efficient algorithms for discrepancy minimization in convex sets"
    - "fair assignment of indivisible objects under ordinal preferences"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-5 (induced by source paper "spherical discrepancy minimization and algorithmic lower bounds for covering the sphere"):
  with members:
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "constructive discrepancy minimization by walking on the edges"
    - "spherical coverage verification"

Connected through hyperedge HE-6 (induced by source paper "on a generalization of iterated and randomized rounding"):
  with members:
    - "a constant factor approximation algorithm for the asymmetric traveling salesman problem"
    - "approximation friendly discrepancy rounding"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "pipage rounding pessimistic estimators and matrix concentration"
    - "entropy optimization and counting"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-7 (induced by source paper "the gram schmidt walk a cure for the banaszczyk blues"):
  with members:
    - "deterministic discrepancy minimization via the multiplicative weight update method"
    - "towards a constructive version of banaszczyk s vector balancing theorem"
    - "approximation friendly discrepancy rounding"
    - "constructive discrepancy minimization for convex sets"
    - "approximating bin packing within o log opt log log opt bins"
    - "the geometry of differential privacy the sparse and approximate cases"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-8 (induced by source paper "proximity results and faster algorithms for integer programming using the steinitz lemma"):
  with members:
    - "on integer programming and convolution"
    - "tight complexity lower bounds for integer linear programming with few constraints"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "on integer programming and the path width of the constraint matrix"

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)