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: "equilibrium computation and robust optimization in zero sum games with submodular structure"
  Abstract: We define a class of zero-sum games with combinatorial structure, where the best response problem of one player is to maximize a submodular function. For example, this class includes security games played on networks, as well as the problem of robustly optimizing a submodular function over the worst case from a set of scenarios. The challenge in computing equilibria is that both players' strategy spaces can be exponentially large. Accordingly, previous algorithms have worst-case exponential runtime and indeed fail to scale up on practical instances. We provide a pseudopolynomial-time algorithm which obtains a guaranteed $(1 - 1/e)^2$-approximate mixed strategy for the maximizing player. Our algorithm only requires access to a weakened version of a best response oracle for the minimizing player which runs in polynomial time. Experimental results for network security games and a robust budget allocation problem confirm that our algorithm delivers near-optimal solutions and scales to much larger instances than was previously possible.

Connected through hyperedge HE-1 (induced by source paper "robust submodular minimization with applications to cooperative modeling"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "approximating some network problems with scenarios"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "polyhedral aspects of submodularity convexity and concavity"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "fast semidifferential based submodular function optimization"

Connected through hyperedge HE-2 (induced by source paper "a unified framework of robust submodular optimization"):
  with members:
    - "near optimal algorithms for hard submodular programs with discounted cooperative costs"
    - "learning from less data a unified data subset selection and active learning framework for computer vision"
    - "robust monotone submodular function maximization"
    - "distributionally robust submodular maximization"
    - "approximating some network problems with scenarios"
    - "a framework towards domain specific video summarization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"

Connected through hyperedge HE-3 (induced by source paper "distributionally robust submodular maximization"):
  with members:
    - "robust monotone submodular function maximization"
    - "robust optimization for non convex objectives"
    - "robust submodular maximization offline and online algorithms"
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (induced by source paper "algorithmic social intervention"):
  with members:
    - "robust optimization for non convex objectives"
    - "robust budget allocation via continuous submodular functions"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "submodular functions from discrete to continous domains"
    - "approximation algorithms for reducing the spectral radius to control epidemic spread"
    - "spectral redemption in clustering sparse networks"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-5 (induced by source paper "defending elections against malicious spread of misinformation"):
  with members:
    - "introduction to online convex optimization"
    - "stochastic submodular maximization the case of coverage functions"
    - "gradient methods for submodular maximization"
    - "robust budget allocation via continuous submodular functions"
    - "elections with few voters candidate control can be easy"
    - "multimode control attacks on elections"

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.GT(Computer Science and Game Theory)