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: "learning from richer human guidance augmenting comparison based learning with feature queries"
  Abstract: We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories is preferable, are much easier for users, and have emerged as an effective alternative. Unfortunately, comparisons are far less informative. We propose that there is much richer information that users can easily provide and that robots ought to leverage. We focus on augmenting comparisons with feature queries, and introduce a unified formalism for treating all answers as observations about the true desired reward. We derive an active query selection algorithm, and test these queries in simulation and on real users. We find that richer, feature-augmented queries can extract more information faster, leading to robots that better match user preferences in their behavior.

Connected through hyperedge HE-1 (induced by source paper "using human ratings for feedback control a supervised learning approach with application to rehabilitation robotics"):
  with members:
    - "constrained inverse optimal control with application to a human manipulation task"
    - "predictive modeling by infinite horizon constrained inverse optimal control with application to a human manipulation task"
    - "cooperative inverse reinforcement learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"

Connected through hyperedge HE-3 (induced by source paper "improving user specifications for robot behavior through active preference learning framework and evaluation"):
  with members:
    - "bayesian active learning for collaborative task specification using equivalence regions"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "april active preference learning based reinforcement learning"
    - "active ranking using pairwise comparisons"
    - "near optimal bayesian active learning with noisy observations"

Connected through hyperedge HE-4 (induced by source paper "bayesian active learning for collaborative task specification using equivalence regions"):
  with members:
    - "active ranking using pairwise comparisons"
    - "near optimal bayesian active learning with noisy observations"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "active learning within constrained environments through imitation of an expert questioner"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "the symbol grounding problem"

Connected through hyperedge HE-6 (induced by source paper "active inverse reward design"):
  with members:
    - "learning dexterous in hand manipulation"
    - "repeated inverse reinforcement learning"
    - "deep bayesian active learning with image data"
    - "inverse reward design"
    - "concrete problems in ai safety"
    - "value iteration networks"
    - "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.AI(Artificial Intelligence)