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: "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"
  Abstract: We study the problem of learning to rank from pairwise preferences, and solve a long-standing open problem that has led to development of many heuristics but no provable results for our particular problem. Given a set $V$ of $n$ elements, we wish to linearly order them given pairwise preference labels. A pairwise preference label is obtained as a response, typically from a human, to the question "which if preferred, u or v?$ for two elements $u,v\in V$. We assume possible non-transitivity paradoxes which may arise naturally due to human mistakes or irrationality. The goal is to linearly order the elements from the most preferred to the least preferred, while disagreeing with as few pairwise preference labels as possible. Our performance is measured by two parameters: The loss and the query complexity (number of pairwise preference labels we obtain). This is a typical learning problem, with the exception that the space from which the pairwise preferences is drawn is finite, consisting of ${n\choose 2}$ possibilities only. We present an active learning algorithm for this problem, with query bounds significantly beating general (non active) bounds for the same error guarantee, while almost achieving the information theoretical lower bound. Our main construct is a decomposition of the input s.t. (i) each block incurs high loss at optimum, and (ii) the optimal solution respecting the decomposition is not much worse than the true opt. The decomposition is done by adapting a recent result by Kenyon and Schudy for a related combinatorial optimization problem to the query efficient setting. We thus settle an open problem posed by learning-to-rank theoreticians and practitioners: What is a provably correct way to sample preference labels? To further show the power and practicality of our solution, we show how to use it in concert with an SVM relaxation.

Connected through hyperedge HE-1 (induced by source paper "can an algorithm be my healthcare proxy"):
  with members:
    - "improving palliative care with deep learning"

Connected through hyperedge HE-2 (induced by source paper "learning reward functions by integrating human demonstrations and preferences"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "simplifying reward design through divide and conquer"
    - "reward learning from human preferences and demonstrations in atari"
    - "batch active preference based learning of reward functions"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "asking easy questions a user friendly approach to active reward learning"):
  with members:
    - "learning an urban air mobility encounter model from expert preferences"
    - "batch active learning using determinantal point processes"
    - "learning from richer human guidance augmenting comparison based learning with feature queries"
    - "batch active preference based learning of reward functions"
    - "efficient test selection in active diagnosis via entropy approximation"
    - "april active preference learning based reinforcement learning"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "fast stochastic ordinal embedding with variance reduction and adaptive step size"):
  with members:
    - "crowdsourced correlation clustering with relative distance comparisons"
    - "learning concept embeddings with combined human machine expertise"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "cost effective hits for relative similarity comparisons"
    - "active ranking using pairwise comparisons"
    - "adaptively learning the crowd kernel"
    - "learning multi modal similarity"

Connected through hyperedge HE-5 (induced by source paper "active ranking with subset wise preferences"):
  with members:
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "assortment optimisation under a general discrete choice model a tight analysis of revenue ordered assortments"
    - "data driven rank breaking for efficient rank aggregation"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "contextual dueling bandits"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"

Connected through hyperedge HE-6 (induced by source paper "adaptive sampling for coarse ranking"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "practical algorithms for best k identification in multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "deep learning the city quantifying urban perception at a global scale"
    - "rank centrality ranking from pair wise comparisons"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-7 (induced by source paper "how many pairwise preferences do we need to rank a graph consistently"):
  with members:
    - "inductive pairwise ranking going beyond the n log n barrier"
    - "birank towards ranking on bipartite graphs"
    - "active ranking using pairwise comparisons"
    - "rank aggregation via nuclear norm minimization"
    - "noisy sorting without resampling"

Connected through hyperedge HE-8 (induced by source paper "batch active preference based learning of reward functions"):
  with members:
    - "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "continuous inverse optimal control with locally optimal examples"
    - "april active preference learning based reinforcement learning"

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.DS(Data Structures and Algorithms)