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: "a nearly linear bound for chasing nested convex bodies"
  Abstract: Friedman and Linial introduced the convex body chasing problem to explore the interplay between geometry and competitive ratio in metrical task systems. In convex body chasing, at each time step $t \in \mathbb{N}$, the online algorithm receives a request in the form of a convex body $K_t \subseteq \mathbb{R}^d$ and must output a point $x_t \in K_t$. The goal is to minimize the total movement between consecutive output points, where the distance is measured in some given norm. #R##N#This problem is still far from being understood, and recently Bansal et al. gave an algorithm for the nested version, where each convex body is contained within the previous one. We propose a different strategy which is $O(d \log d)$-competitive algorithm for this nested convex body chasing problem, improving substantially over previous work. Our algorithm works for any norm. This result is almost tight, given an $\Omega(d)$ lower bound for the $\ell_{\infty}$.

Connected through hyperedge HE-1 (induced by source paper "path length bounds for gradient descent and flow"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "improved path length regret bounds for bandits"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "more adaptive algorithms for adversarial bandits"
    - "chasing nested convex bodies nearly optimally"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "online optimization with predictions and non convex losses"):
  with members:
    - "chasing convex bodies optimally"
    - "introduction to online convex optimization"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "improved path length regret bounds for bandits"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "potential function proofs for first order methods"
    - "k server via multiscale entropic regularization"

Connected through hyperedge HE-3 (induced by source paper "beyond online balanced descent an optimal algorithm for smoothed online optimization"):
  with members:
    - "introduction to online convex optimization"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "competitively chasing convex bodies"
    - "better bounds for online line chasing"
    - "potential function proofs for first order methods"
    - "nested convex bodies are chaseable"
    - "online convex optimization using predictions"

Connected through hyperedge HE-4 (induced by source paper "chasing convex bodies with linear competitive ratio"):
  with members:
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "competitively chasing convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "nested convex bodies are chaseable"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"

Connected through hyperedge HE-5 (induced by source paper "chasing convex bodies optimally"):
  with members:
    - "introduction to online convex optimization"
    - "competitively chasing convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"

Connected through hyperedge HE-6 (induced by source paper "competitively chasing convex bodies"):
  with members:
    - "introduction to online convex optimization"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"
    - "nested convex bodies are chaseable"

Connected through hyperedge HE-7 (induced by source paper "smoothed online optimization for regression and control"):
  with members:
    - "introduction to online convex optimization"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "online convex optimization using predictions"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"

Connected through hyperedge HE-8 (induced by source paper "better bounds for online line chasing"):
  with members:
    - "competitively chasing convex bodies"
    - "chasing nested convex bodies nearly optimally"
    - "nested convex bodies are chaseable"
    - "the generalized work function algorithm is competitive for the generalized 2 server 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)