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: "nested convex bodies are chaseable"
  Abstract: In the Convex Body Chasing problem, we are given an initial point $v_0$ in $R^d$ and an online sequence of $n$ convex bodies $F_1, ..., F_n$. When we receive $F_i$, we are required to move inside $F_i$. Our goal is to minimize the total distance travelled. This fundamental online problem was first studied by Friedman and Linial (DCG 1993). They proved an $\Omega(\sqrt{d})$ lower bound on the competitive ratio, and conjectured that a competitive ratio depending only on d is possible. However, despite much interest in the problem, the conjecture remains wide open. #R##N#We consider the setting in which the convex bodies are nested: $F_1 \supset ... \supset F_n$. The nested setting is closely related to extending the online LP framework of Buchbinder and Naor (ESA 2005) to arbitrary linear constraints. Moreover, this setting retains much of the difficulty of the general setting and captures an essential obstacle in resolving Friedman and Linial's conjecture. In this work, we give the first $f(d)$-competitive algorithm for chasing nested convex bodies in $R^d$.

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"
    - "chasing nested convex bodies nearly optimally"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-2 (induced by source paper "competitive online optimization under inventory constraints"):
  with members:
    - "balancing cost and dissatisfaction in online ev charging under real time pricing"
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "online offering strategies for storage assisted renewable power producer in hour ahead market"
    - "online convex optimization using predictions"
    - "a tale of two metrics simultaneous bounds on competitiveness and regret"
    - "peak aware online economic dispatching for microgrids"
    - "the submodular secretary problem goes linear"
    - "online energy generation scheduling for microgrids with intermittent energy sources and co generation"

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"
    - "competitively chasing convex bodies"
    - "better bounds for online line chasing"
    - "potential function proofs for first order methods"
    - "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"
    - "competitively chasing convex bodies"
    - "smoothed online convex optimization in high dimensions via online balanced descent"
    - "chasing nested convex bodies nearly optimally"
    - "the generalized work function algorithm is competitive for the generalized 2 server problem"

Connected through hyperedge HE-5 (induced by source paper "a ptas for euclidean tsp with hyperplane neighborhoods"):
  with members:
    - "chasing convex bodies optimally"
    - "competitively chasing convex bodies"
    - "a ptas for tsp with neighborhoods among fat regions in the plane"
    - "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-6 (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"
    - "chasing nested convex bodies nearly optimally"
    - "fusible hsts and the randomized k server conjecture"
    - "k server via multiscale entropic regularization"

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"
    - "competitively chasing convex bodies"
    - "chasing nested convex bodies nearly optimally"
    - "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)