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: "chasing nested convex bodies nearly optimally"
  Abstract: The convex body chasing problem, introduced by Friedman and Linial, is a competitive analysis problem on any normed vector space. In convex body chasing, for each timestep $t\in\mathbb N$, a convex body $K_t\subseteq \mathbb R^d$ is given as a request, and the player picks a point $x_t\in K_t$. The player aims to ensure that the total distance $\sum_{t=0}^{T-1}||x_t-x_{t+1}||$ is within a bounded ratio of the smallest possible offline solution. #R##N#In this work, we consider the nested version of the problem, in which the sequence $(K_t)$ must be decreasing. For Euclidean spaces, we consider a memoryless algorithm which moves to the so-called Steiner point, and show that in a certain sense it is exactly optimal among memoryless algorithms. For general finite dimensional normed spaces, we combine the Steiner point and our recent previous algorithm to obtain a new algorithm which is nearly optimal for all $\ell^p_d$ spaces with $p\geq 1$, closing a polynomial gap.

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"
    - "a nearly linear bound for chasing nested convex bodies"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "more adaptive algorithms for adversarial bandits"
    - "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"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "potential function proofs for first order methods"
    - "k server via multiscale entropic regularization"

Connected through hyperedge HE-3 (induced by source paper "chasing convex bodies with linear competitive ratio"):
  with members:
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "competitively chasing convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "nested convex bodies are chaseable"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"

Connected through hyperedge HE-4 (induced by source paper "chasing convex bodies optimally"):
  with members:
    - "introduction to online convex optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "competitively chasing convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"

Connected through hyperedge HE-5 (induced by source paper "competitively chasing convex bodies"):
  with members:
    - "introduction to online convex optimization"
    - "a nearly linear bound for chasing nested convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"
    - "nested convex bodies are chaseable"

Connected through hyperedge HE-6 (induced by source paper "better bounds for online line chasing"):
  with members:
    - "a nearly linear bound for chasing nested convex bodies"
    - "competitively chasing convex bodies"
    - "nested convex bodies are chaseable"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"

Connected through hyperedge HE-7 (induced by source paper "nested convex bodies are chaseable"):
  with members:
    - "chasing convex bodies optimally"
    - "chasing convex bodies with linear competitive ratio"
    - "a nearly linear bound for chasing nested convex bodies"
    - "competitively chasing convex bodies"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"
    - "ramsey type theorems for metric spaces with applications to online problems"
    - "better algorithms for unfair metrical task systems and applications"

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)