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: "near optimal nonmyopic value of information in graphical models"
  Abstract: A fundamental issue in real-world systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present the first efficient randomized algorithm providing a constant factor (1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high confidence. The algorithm leverages the theory of submodular functions, in combination with a polynomial bound on sample complexity. We furthermore prove that no polynomial time algorithm can provide a constant factor approximation better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of the effectiveness of our method on two complex real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "on the unreasonable effectiveness of the greedy algorithm greedy adapts to sharpness"):
  with members:
    - "guess free maximization of submodular and linear sums"
    - "stability and recovery for independence systems"
    - "constrained submodular maximization beyond 1 e"
    - "stability of influence maximization"
    - "deterministic algorithms for submodular maximization problems"
    - "computational feasibility of clustering under clusterability assumptions"
    - "lazier than lazy greedy"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"

Connected through hyperedge HE-2 (induced by source paper "discrete signal processing with set functions"):
  with members:
    - "fast iterative combinatorial auctions via bayesian learning"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discrete signal processing on graphs sampling theory"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-3 (induced by source paper "adaptive sequence submodularity"):
  with members:
    - "submodular streaming in all its glory tight approximation minimum memory and low adaptive complexity"
    - "beyond adaptive submodularity approximation guarantees of greedy policy with adaptive submodularity ratio"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "data summarization at scale a two stage submodular approach"
    - "submodular maximization with optimal approximation adaptivity and query complexity"
    - "do less get more streaming submodular maximization with subsampling"
    - "beyond 1 2 approximation for submodular maximization on massive data streams"
    - "submodular maximization with nearly optimal approximation and adaptivity in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "submodular maximization beyond non negativity guarantees fast algorithms and applications"):
  with members:
    - "fast maximization of non submodular monotonic functions on the integer lattice"
    - "guess free maximization of submodular and linear sums"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "approximate supermodularity bounds for experimental design"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "lazier than lazy greedy"

Connected through hyperedge HE-5 (induced by source paper "value of information in probabilistic logic programs"):
  with members:
    - "an anytime algorithm for decision making under uncertainty"
    - "myopic value of information in influence diagrams"
    - "an approximate nonmyopic computation for value of information"
    - "the pita system tabling and answer subsumption for reasoning under uncertainty"
    - "optimal value of information in graphical models"
    - "reasoning and planning with sensing actions incomplete information and static causal laws using answer set programming"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "cost sensitive classification empirical evaluation of a hybrid genetic decision tree induction algorithm"

Connected through hyperedge HE-6 (induced by source paper "accelerated experimental design for pairwise comparisons"):
  with members:
    - "lazier than lazy greedy"
    - "identifying users from their rating patterns"
    - "active ranking using pairwise comparisons"
    - "inferring networks of diffusion and influence"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-8 (induced by source paper "a marketplace for data an algorithmic solution"):
  with members:
    - "introduction to online convex optimization"
    - "adversarially robust generalization requires more data"
    - "statistical cost sharing"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "learning in auctions regret is hard envy is easy"
    - "bounding the estimation error of sampling based shapley value approximation with without stratifying"

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.AI(Artificial Intelligence)