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: "interactive submodular set cover"
  Abstract: We introduce a natural generalization of submodular set cover and exact active learning with a finite hypothesis class (query learning). We call this new problem interactive submodular set cover. Applications include advertising in social networks with hidden information. We give an approximation guarantee for a novel greedy algorithm and give a hardness of approximation result which matches up to constant factors. We also discuss negative results for simpler approaches and present encouraging early experimental results.

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"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "adaptivity in adaptive submodularity"):
  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"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "unconstrained submodular maximization with constant adaptive complexity"
    - "non monotone submodular maximization in exponentially fewer iterations"
    - "stochastic submodular cover with limited adaptivity"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"
    - "discrete adversarial attacks and submodular optimization with applications to text classification"

Connected through hyperedge HE-3 (induced by source paper "overcoming practical issues of deep active learning and its applications on named entity recognition"):
  with members:
    - "practical obstacles to deploying active learning"
    - "deep bayesian active learning for natural language processing results of a large scale empirical study"
    - "learning how to active learn a deep reinforcement learning approach"
    - "learning active learning from data"
    - "fast and accurate entity recognition with iterated dilated convolutions"
    - "learning algorithms for active learning"
    - "deep learning scaling is predictable empirically"
    - "a method for stopping active learning based on stabilizing predictions and the need for user adjustable stopping"

Connected through hyperedge HE-4 (induced by source paper "data sampling for graph based unsupervised learning convex and greedy optimization"):
  with members:
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "optimal solutions for sparse principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "batched stochastic bayesian optimization via combinatorial constraints design"):
  with members:
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial active learning"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "learning to generate chairs tables and cars with convolutional networks"
    - "active learning with distributional estimates"

Connected through hyperedge HE-7 (induced by source paper "streaming robust submodular maximization a partitioned thresholding approach"):
  with members:
    - "robust monotone submodular function maximization"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "submodular learning and covering with response dependent costs"):
  with members:
    - "trading off worst and expected cost in decision tree problems and a value dependent model"
    - "auditing active learning with outcome dependent query costs"
    - "greedy feature selection for subspace clustering"
    - "efficient active learning of halfspaces an aggressive approach"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

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)