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: "interactive learning from policy dependent human feedback"
  Abstract: For agents and robots to become more useful, they must be able to quickly learn from non-technical users. This paper investigates the problem of interactively learning behaviors communicated by a human teacher using positive and negative feedback. Much previous work on this problem has made the assumption that people provide feedback for decisions that is dependent on the behavior they are teaching and is independent from the learner's current policy. We present empirical results that show this assumption to be false---whether human trainers give a positive or negative feedback for a decision is influenced by the learner's current policy. We argue that policy-dependent feedback, in addition to being commonplace, enables useful training strategies from which agents should benefit. Based on this insight, we introduce Convergent Actor-Critic by Humans (COACH), an algorithm for learning from policy-dependent feedback that converges to a local optimum. Finally, we demonstrate that COACH can successfully learn multiple behaviors on a physical robot, even with noisy image features.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "fresh interactive reward shaping in high dimensional state spaces using human feedback"):
  with members:
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "deep reinforcement learning from policy dependent human feedback"
    - "potential based advice for stochastic policy learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "hg dagger interactive imitation learning with human experts"
    - "dqn tamer human in the loop reinforcement learning with intractable feedback"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep tamer interactive agent shaping in high dimensional state spaces"

Connected through hyperedge HE-3 (induced by source paper "facial feedback for reinforcement learning a case study and offline analysis using the tamer framework"):
  with members:
    - "face valuing training user interfaces with facial expressions and reinforcement learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "deep interactive reinforcement learning for path following of autonomous underwater vehicle"):
  with members:
    - "prioritized experience replay"
    - "deep learning in neural networks"

Connected through hyperedge HE-5 (induced by source paper "self regulated interactive sequence to sequence learning"):
  with members:
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "joey nmt a minimalist nmt toolkit for novices"
    - "reliability and learnability of human bandit feedback for sequence to sequence reinforcement learning"
    - "learning to teach with dynamic loss functions"
    - "a reinforcement learning approach to interactive predictive neural machine translation"
    - "active learning for interactive neural machine translation of data streams"
    - "learning from chunk based feedback in neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "reinforcing an image caption generator using off line human feedback"):
  with members:
    - "vifidel evaluating the visual fidelity of image descriptions"
    - "neural naturalist generating fine grained image comparisons"
    - "informative image captioning with external sources of information"
    - "graph rise graph regularized image semantic embedding"
    - "decoupled box proposal and featurization with ultrafine grained semantic labels improve image captioning and visual question answering"
    - "neural baby talk"
    - "reward learning from human preferences and demonstrations in atari"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"

Connected through hyperedge HE-7 (induced by source paper "faster and safer training by embedding high level knowledge into deep reinforcement learning"):
  with members:
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "recent advances in imitation learning from observation"
    - "hierarchical imitation and reinforcement learning"
    - "parameter transfer unit for deep neural networks"
    - "constrained policy optimization"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "safe reinforcement learning via shielding"
    - "a deep hierarchical approach to lifelong learning in minecraft"

Connected through hyperedge HE-8 (induced by source paper "preferences implicit in the state of the world"):
  with members:
    - "imitating latent policies from observation"
    - "penalizing side effects using stepwise relative reachability"
    - "measuring and avoiding side effects using relative reachability"
    - "visual reinforcement learning with imagined goals"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "learning to understand goal specifications by modelling reward"
    - "one shot imitation from observing humans via domain adaptive meta 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)