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: "the simplex algorithm is np mighty"
  Abstract: We propose to classify the power of algorithms by the complexity of the problems that they can be used to solve. Instead of restricting to the problem a particular algorithm was designed to solve explicitly, however, we include problems that, with polynomial overhead, can be solved 'implicitly' during the algorithm's execution. For example, we allow to solve a decision problem by suitably transforming the input, executing the algorithm, and observing whether a specific bit in its internal configuration ever switches during the execution. We show that the Simplex Method, the Network Simplex Method (both with Dantzig's original pivot rule), and the Successive Shortest Path Algorithm are NP-mighty, that is, each of these algorithms can be used to solve any problem in NP. This result casts a more favorable light on these algorithms' exponential worst-case running times. Furthermore, as a consequence of our approach, we obtain several novel hardness results. For example, for a given input to the Simplex Algorithm, deciding whether a given variable ever enters the basis during the algorithm's execution and determining the number of iterations needed are both NP-hard problems. Finally, we close a long-standing open problem in the area of network flows over time by showing that earliest arrival flows are NP-hard to obtain.

Connected through hyperedge HE-1 (induced by source paper "algorithms for flows over time with scheduling costs"):
  with members:
    - "pricing in resource allocation games based on duality gaps"
    - "nash flows over time with spillback"
    - "a stackelberg strategy for routing flow over time"

Connected through hyperedge HE-2 (induced by source paper "on the computational power of online gradient descent"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "on the computational complexity of limit cycles in dynamical systems"
    - "on simplex pivoting rules and complexity theory"
    - "the complexity of the simplex method"
    - "the complexity of the homotopy method equilibrium selection and lemke howson solutions"

Connected through hyperedge HE-3 (induced by source paper "the niceness of unique sink orientations"):
  with members:
    - "on simplex pivoting rules and complexity theory"

Connected through hyperedge HE-4 (induced by source paper "the complexity of all switches strategy improvement"):
  with members:
    - "on simplex pivoting rules and complexity theory"
    - "the complexity of the simplex method"
    - "an exponential lower bound for the latest deterministic strategy iteration algorithms"
    - "non oblivious strategy improvement"
    - "the complexity of the homotopy method equilibrium selection and lemke howson solutions"
    - "exponential lower bounds for policy iteration"
    - "linear complementarity algorithms for infinite games"

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.DM(Discrete Mathematics)