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: "competitively chasing convex bodies"
  Abstract: Let $\mathcal{F}$ be a family of sets in some metric space. In the $\mathcal{F}$-chasing problem, an online algorithm observes a request sequence of sets in $\mathcal{F}$ and responds (online) by giving a sequence of points in these sets. The movement cost is the distance between consecutive such points. The competitive ratio is the worst case ratio (over request sequences) between the total movement of the online algorithm and the smallest movement one could have achieved by knowing in advance the request sequence. The family $\mathcal{F}$ is said to be chaseable if there exists an online algorithm with finite competitive ratio. In 1991, Linial and Friedman conjectured that the family of convex sets in Euclidean space is chaseable. We prove this conjecture.

Connected through hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory"):
  with members:
    - "chasing convex bodies optimally"
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "competitive online optimization under inventory constraints"
    - "online control with adversarial disturbances"
    - "beyond online balanced descent an optimal algorithm for smoothed online optimization"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "online inventory management with application to energy procurement in data centers"

Connected through hyperedge HE-2 (induced by source paper "improved path length regret bounds for bandits"):
  with members:
    - "chasing convex bodies optimally"
    - "black box reductions for parameter free online learning in banach spaces"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "adaptive online learning in dynamic environments"
    - "tracking the best expert in non stationary stochastic environments"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"

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"
    - "a nearly linear bound for chasing nested convex bodies"
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "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"
    - "a nearly linear bound for chasing nested 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"
    - "a nearly linear bound for chasing nested 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 "a ptas for euclidean tsp with hyperplane neighborhoods"):
  with members:
    - "chasing convex bodies optimally"
    - "a ptas for tsp with neighborhoods among fat regions in the plane"
    - "nested convex bodies are chaseable"
    - "the traveling salesman problem low dimensionality implies a polynomial time approximation scheme"
    - "the traveling salesman problem for lines balls and planes"
    - "the traveling salesman problem for lines and rays in the plane"
    - "approximation algorithms for tsp with neighborhoods in the plane"

Connected through hyperedge HE-7 (induced by source paper "better bounds for online line chasing"):
  with members:
    - "a nearly linear bound for chasing nested 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"

Connected through hyperedge HE-8 (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"
    - "chasing nested convex bodies nearly optimally"
    - "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)