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: "active classification with comparison queries"
  Abstract: We study an extension of active learning in which the learning algorithm may ask the annotator to compare the distances of two examples from the boundary of their label-class. For example, in a recommendation system application (say for restaurants), the annotator may be asked whether she liked or disliked a specific restaurant (a label query); or which one of two restaurants did she like more (a comparison query). #R##N#We focus on the class of half spaces, and show that under natural assumptions, such as large margin or bounded bit-description of the input examples, it is possible to reveal all the labels of a sample of size $n$ using approximately $O(\log n)$ queries. This implies an exponential improvement over classical active learning, where only label queries are allowed. We complement these results by showing that if any of these assumptions is removed then, in the worst case, $\Omega(n)$ queries are required. #R##N#Our results follow from a new general framework of active learning with additional queries. We identify a combinatorial dimension, called the \emph{inference dimension}, that captures the query complexity when each additional query is determined by $O(1)$ examples (such as comparison queries, each of which is determined by the two compared examples). Our results for half spaces follow by bounding the inference dimension in the cases discussed above.

Connected through hyperedge HE-1 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise"):
  with members:
    - "noise tolerant reliable active classification with comparison queries"
    - "a modern introduction to online learning"
    - "adaptive hard thresholding for near optimal consistent robust regression"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "efficient active learning of sparse halfspaces"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "revisiting perceptron efficient and label optimal learning of halfspaces"

Connected through hyperedge HE-2 (induced by source paper "noise tolerant reliable active classification with comparison queries"):
  with members:
    - "the power of comparisons for actively learning linear classifiers"
    - "sorting by swaps with noisy comparisons"
    - "generalized comparison trees for point location problems"
    - "optimal sorting with persistent comparison errors"
    - "efficient learning of linear separators under bounded noise"
    - "minimax analysis of active learning"
    - "active and passive learning of linear separators under log concave distributions"
    - "sorting from noisy information"

Connected through hyperedge HE-3 (induced by source paper "the power of comparisons for actively learning linear classifiers"):
  with members:
    - "generalized comparison trees for point location problems"
    - "near optimal linear decision trees for k sum and related problems"
    - "conspiracies between learning algorithms circuit lower bounds and pseudorandomness"
    - "efficient learning of linear separators under bounded noise"
    - "active and passive learning of linear separators under log concave distributions"
    - "adaptively learning the crowd kernel"

Connected through hyperedge HE-4 (induced by source paper "zeroth order non convex optimization with dueling choice bandits"):
  with members:
    - "multi dueling bandits with dependent arms"
    - "hyperparameter optimization a spectral approach"
    - "on kernelized multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-5 (induced by source paper "thresholding bandit problem with both duels and pulls"):
  with members:
    - "thresholding bandits with augmented ucb"
    - "adaptive multiple arm identification"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "statistical decision making for optimal budget allocation in crowd labeling"
    - "multiple identifications in multi armed bandits"

Connected through hyperedge HE-6 (induced by source paper "near optimal linear decision trees for k sum and related problems"):
  with members:
    - "generalized comparison trees for point location problems"
    - "the decision tree complexity for k sum is at most nearly quadratic"
    - "improved bounds for 3sum k sum and linear degeneracy"
    - "clustered integer 3sum via additive combinatorics"
    - "solving k sum using few linear queries"
    - "threesomes degenerates and love triangles"
    - "higher lower bounds from the 3sum conjecture"
    - "faster all pairs shortest paths via circuit complexity"

Connected through hyperedge HE-7 (induced by source paper "decomposing arrangements of hyperplanes vc dimension combinatorial dimension and point location"):
  with members:
    - "generalized comparison trees for point location problems"
    - "near optimal linear decision trees for k sum and related problems"
    - "shortest path in a polygon using sublinear space"

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)