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: "constructive discrepancy minimization with hereditary l2 guarantees"
  Abstract: In discrepancy minimization problems, we are given a family of sets $\mathcal{S} = \{S_1,\dots,S_m\}$, with each $S_i \in \mathcal{S}$ a subset of some universe $U = \{u_1,\dots,u_n\}$ of $n$ elements. The goal is to find a coloring $\chi : U \to \{-1,+1\}$ of the elements of $U$ such that each set $S \in \mathcal{S}$ is colored as evenly as possible. Two classic measures of discrepancy are $\ell_\infty$-discrepancy defined as $\textrm{disc}_\infty(\mathcal{S},\chi):=\max_{S \in \mathcal{S}} | \sum_{u_i \in S} \chi(u_i) |$ and $\ell_2$-discrepancy defined as $\textrm{disc}_2(\mathcal{S},\chi):=\sqrt{(1/|\mathcal{S}|)\sum_{S \in \mathcal{S}} \left(\sum_{u_i \in S}\chi(u_i)\right)^2}$. Breakthrough work by Bansal gave a polynomial time algorithm, based on rounding an SDP, for finding a coloring $\chi$ such that $\textrm{disc}_\infty(\mathcal{S},\chi) = O(\lg n \cdot \textrm{herdisc}_\infty(\mathcal{S}))$ where $\textrm{herdisc}_\infty(\mathcal{S})$ is the hereditary $\ell_\infty$-discrepancy of $\mathcal{S}$. We complement his work by giving a simple $O((m+n)n^2)$ time algorithm for finding a coloring $\chi$ such $\textrm{disc}_2(\mathcal{S},\chi) = O(\sqrt{\lg n} \cdot \textrm{herdisc}_2(\mathcal{S}))$ where $\textrm{herdisc}_2(\mathcal{S})$ is the hereditary $\ell_2$-discrepancy of $\mathcal{S}$. Interestingly, our algorithm avoids solving an SDP and instead relies on computing eigendecompositions of matrices. Moreover, we use our ideas to speed up the Edge-Walk algorithm by Lovett and Meka [SICOMP'15]. To prove that our algorithm has the claimed guarantees, we show new inequalities relating $\textrm{herdisc}_\infty$ and $\textrm{herdisc}_2$ to the eigenvalues of the matrix corresponding to $\mathcal{S}$. Our inequalities improve over previous work by Chazelle and Lvov, and by Matousek et al. Finally, we also implement our algorithm and show that it far outperforms random sampling.

Connected through hyperedge HE-1 (induced by source paper "generalised pattern matching revisited"):
  with members:
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "algorithmic discrepancy beyond partial coloring"
    - "popular conjectures imply strong lower bounds for dynamic problems"
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

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)