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: "batch active preference based learning of reward functions"
  Abstract: Data generation and labeling are usually an expensive part of learning for robotics. While active learning methods are commonly used to tackle the former problem, preference-based learning is a concept that attempts to solve the latter by querying users with preference questions. In this paper, we will develop a new algorithm, batch active preference-based learning, that enables efficient learning of reward functions using as few data samples as possible while still having short query generation times. We introduce several approximations to the batch active learning problem, and provide theoretical guarantees for the convergence of our algorithms. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We then showcase our algorithm in a study to learn human users' preferences.

Connected through hyperedge HE-1 (induced by source paper "interactive robot training for non markov tasks"):
  with members:
    - "planning with uncertain specifications puns"
    - "planning with state abstractions for non markovian task specifications"
    - "a survey of reinforcement learning informed by natural language"
    - "learning task specifications from demonstrations"
    - "environment independent task specifications via gltl"
    - "interpretable apprenticeship learning with temporal logic specifications"

Connected through hyperedge HE-2 (induced by source paper "learning human objectives by evaluating hypothetical behavior"):
  with members:
    - "improving safety in reinforcement learning using model based architectures and human intervention"
    - "model based reinforcement learning for atari"
    - "risk aware active inverse reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "safe exploration in continuous action spaces"
    - "supervising strong learners by amplifying weak experts"
    - "scalable agent alignment via reward modeling a research direction"
    - "active inverse reward design"

Connected through hyperedge HE-3 (induced by source paper "batch active learning using determinantal point processes"):
  with members:
    - "diverse mini batch active learning"
    - "uncertainty sampling is preconditioned stochastic gradient descent on zero one loss"
    - "active model learning and diverse action sampling for task and motion planning"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "determinantal point processes for mini batch diversification"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-4 (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"
    - "inverse reward design"
    - "one shot imitation learning"
    - "proximal policy optimization algorithms"
    - "learning preferences for manipulation tasks from online coactive feedback"

Connected through hyperedge HE-5 (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"
    - "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"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-6 (induced by source paper "learning an urban air mobility encounter model from expert preferences"):
  with members:
    - "learning probabilistic trajectory models of aircraft in terminal airspace from position data"

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)