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: "generalized binary search for split neighborly problems"
  Abstract: In sequential hypothesis testing, Generalized Binary Search (GBS) greedily chooses the test with the highest information gain at each step. It is known that GBS obtains the gold standard query cost of $O(\log n)$ for problems satisfying the $k$-neighborly condition, which requires any two tests to be connected by a sequence of tests where neighboring tests disagree on at most $k$ hypotheses. In this paper, we introduce a weaker condition, split-neighborly, which requires that for the set of hypotheses two neighbors disagree on, any subset is splittable by some test. For four problems that are not $k$-neighborly for any constant $k$, we prove that they are split-neighborly, which allows us to obtain the optimal $O(\log n)$ worst-case query cost.

Connected through hyperedge HE-1 (induced by source paper "evolving neural networks through a reverse encoding tree"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "vine an open source interactive data visualization tool for neuroevolution"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "anti jamming communications using spectrum waterfall a deep reinforcement learning approach"
    - "complex networks from simple rewrite systems"

Connected through hyperedge HE-2 (induced by source paper "flattening a hierarchical clustering through active learning"):
  with members:
    - "diameter based active learning"
    - "near optimal bayesian active learning with correlated and noisy tests"
    - "clustering with same cluster queries"
    - "local algorithms for interactive clustering"
    - "efficient active learning of halfspaces an aggressive approach"
    - "agnostic active learning without constraints"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "importance weighted active learning"

Connected through hyperedge HE-3 (induced by source paper "a tight analysis of greedy yields subexponential time approximation for uniform decision tree"):
  with members:
    - "approximation algorithms for optimal decision trees and adaptive tsp problems"
    - "inapproximability of vc dimension and littlestone s dimension"
    - "inapproximability of nash equilibrium"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "noisy bayesian active learning"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

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)