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: "online submodular maximization under a matroid constraint with application to learning assignments"
  Abstract: Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize the value of the ranking? These applications exhibit strong diminishing returns: Redundancy decreases the marginal utility of each ad or information source. We show that these and other problems can be formalized as repeatedly selecting an assignment of items to positions to maximize a sequence of monotone submodular functions that arrive one by one. We present an efficient algorithm for this general problem and analyze it in the no-regret model. Our algorithm possesses strong theoretical guarantees, such as a performance ratio that converges to the optimal constant of 1 1=e. We empirically evaluate our algorithm on two real-world online optimization problems on the web: ad allocation with submodular utilities, and dynamically ranking blogs to detect information cascades. Finally, we present a second algorithm that handles the more general case in which the feasible sets are given by a matroid constraint, while still maintaining a 1 1=e asymptotic performance ratio.

Connected through hyperedge HE-1 (induced by source paper "powerset convolutional neural networks"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "on the limitations of representing functions on sets"
    - "learning a sat solver from single bit supervision"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep sets"
    - "simplicial closure and higher order link prediction"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "neural message passing for quantum chemistry"

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"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "an optimal algorithm for bandit convex optimization"
    - "submodular functions from discrete to continous domains"
    - "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 "maxhedge maximising a maximum online"):
  with members:
    - "service placement with provable guarantees in heterogeneous edge computing systems"
    - "online facility location with deletions"
    - "online learning of combinatorial objects via extended formulation"

Connected through hyperedge HE-4 (induced by source paper "no regret algorithms for online k submodular maximization"):
  with members:
    - "introduction to online convex optimization"
    - "an optimal algorithm for online unconstrained submodular maximization"
    - "derandomization for k submodular maximization"
    - "maximizing k submodular functions and beyond"
    - "on maximizing a monotone k submodular function subject to a matroid constraint"
    - "improved approximation algorithms for k submodular function maximization"
    - "towards minimizing k submodular functions"

Connected through hyperedge HE-5 (induced by source paper "robust submodular maximization offline and online algorithms"):
  with members:
    - "robust monotone submodular function maximization"
    - "robust optimization for non convex objectives"
    - "constrained submodular maximization beyond 1 e"
    - "deterministic algorithms for submodular maximization problems"
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-6 (induced by source paper "combinatorial prophet inequalities"):
  with members:
    - "beyond matroids secretary problem and prophet inequality with general constraints"
    - "bayesian budget feasibility with posted pricing"
    - "secretary problems with non uniform arrival order"
    - "streaming algorithms for submodular function maximization"
    - "online submodular maximization with preemption"
    - "online contention resolution schemes"
    - "building a good team secretary problems and the supermodular degree"
    - "locally adaptive optimization adaptive seeding for monotone submodular functions"

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)