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: "optimal analysis of an online algorithm for the bipartite matching problem on a line"
  Abstract: In the online metric bipartite matching problem, we are given a set $S$ of server locations in a metric space. Requests arrive one at a time, and on its arrival, we need to immediately and irrevocably match it to a server at a cost which is equal to the distance between these locations. A $\alpha$-competitive algorithm will assign requests to servers so that the total cost is at most $\alpha$ times the cost of $M_{OPT}$ where $M_{OPT}$ is the minimum cost matching between $S$ and $R$. #R##N#We consider this problem in the adversarial model for the case where $S$ and $R$ are points on a line and $|S|=|R|=n$. We improve the analysis of the deterministic Robust Matching Algorithm (RM-Algorithm, Nayyar and Raghvendra FOCS'17) from $O(\log^2 n)$ to an optimal $\Theta(\log n)$. Previously, only a randomized algorithm under a weaker oblivious adversary achieved a competitive ratio of $O(\log n)$ (Gupta and Lewi, ICALP'12). The well-known Work Function Algorithm (WFA) has a competitive ratio of $O(n)$ and $\Omega(\log n)$ for this problem. Therefore, WFA cannot achieve an asymptotically better competitive ratio than the RM-Algorithm.

Connected through hyperedge HE-1 (induced by source paper "online metric algorithms with untrusted predictions"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "near optimal bounds for online caching with machine learned advice"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "online computation with untrusted advice"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "revenue optimization with approximate bid predictions"
    - "competitive paging algorithms"

Connected through hyperedge HE-2 (induced by source paper "stochastic online metric matching"):
  with members:
    - "online matching with general arrivals"
    - "tight competitive ratios of classic matching algorithms in the fully online model"
    - "how to match when all vertices arrive online"
    - "online vertex weighted bipartite matching beating 1 1 e with random arrivals"
    - "stochastic k server how should uber work"
    - "online allocation with traffic spikes mixing adversarial and stochastic models"
    - "new algorithms better bounds and a novel model for online stochastic matching"
    - "near optimum online ad allocation for targeted advertising"

Connected through hyperedge HE-3 (induced by source paper "permutation strikes back the power of recourse in online metric matching"):
  with members:
    - "stochastic online metric matching"
    - "maintaining perfect matchings at low cost"
    - "fully dynamic bin packing with limited repacking"
    - "a match in time saves nine deterministic online matching with delays"
    - "a collection of lower bounds for online matching on the line"
    - "online and dynamic algorithms for set cover"
    - "online matching haste makes waste"
    - "new algorithms better bounds and a novel model for online stochastic matching"

Connected through hyperedge HE-4 (induced by source paper "maintaining perfect matchings at low cost"):
  with members:
    - "online steiner tree with deletions"
    - "the power of deferral maintaining a constant competitive steiner tree online"

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.CG(Computational Geometry)