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 dr submodular maximization and applications to provable mean field inference"
  Abstract: Mean field inference in probabilistic models is generally a highly nonconvex problem. Existing optimization methods, e.g., coordinate ascent algorithms, can only generate local optima. #R##N#In this work we propose provable mean filed methods for probabilistic log-submodular models and its posterior agreement (PA) with strong approximation guarantees. The main algorithmic technique is a new Double Greedy scheme, termed DR-DoubleGreedy, for continuous DR-submodular maximization with box-constraints. It is a one-pass algorithm with linear time complexity, reaching the optimal 1/2 approximation ratio, which may be of independent interest. We validate the superior performance of our algorithms against baseline algorithms on both synthetic and real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "black box submodular maximization discrete and continuous settings"):
  with members:
    - "introduction to online convex optimization"
    - "online continuous submodular maximization from full information to bandit feedback"
    - "black box adversarial attacks with limited queries and information"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "continuous dr submodular maximization structure and algorithms"
    - "robust budget allocation via continuous submodular functions"

Connected through hyperedge HE-2 (induced by source paper "online continuous submodular maximization from full information to bandit feedback"):
  with members:
    - "black box submodular maximization discrete and continuous settings"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "an optimal algorithm for bandit convex optimization"
    - "submodular functions from discrete to continous domains"
    - "online submodular maximization under a matroid constraint with application to learning assignments"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "projection free online learning"

Connected through hyperedge HE-3 (induced by source paper "parallel algorithm for non monotone dr submodular maximization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "continuous dr submodular maximization structure and algorithms"
    - "non monotone dr submodular function maximization"
    - "guaranteed non convex optimization submodular maximization over continuous domains"

Connected through hyperedge HE-4 (induced by source paper "unconstrained submodular maximization with constant adaptive complexity"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "non monotone submodular maximization with nearly optimal adaptivity complexity"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "do less get more streaming submodular maximization with subsampling"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "gradient methods for submodular maximization"
    - "greed is good near optimal submodular maximization via greedy optimization"

Connected through hyperedge HE-5 (induced by source paper "submodular maximization with matroid and packing constraints in parallel"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "optimal algorithms for continuous non monotone submodular and dr submodular maximization"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "constrained submodular maximization via a non symmetric technique"

Connected through hyperedge HE-6 (induced by source paper "a parallel double greedy algorithm for submodular maximization"):
  with members:
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "submodular maximization with matroid and packing constraints in parallel"
    - "submodular function maximization in parallel via the multilinear relaxation"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "an optimal approximation for submodular maximization under a matroid constraint in the adaptive complexity model"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

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.LG(Machine Learning)