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: "simultaneous control and human feedback in the training of a robotic agent with actor critic reinforcement learning"
  Abstract: This paper contributes a preliminary report on the advantages and disadvantages of incorporating simultaneous human control and feedback signals in the training of a reinforcement learning robotic agent. While robotic human-machine interfaces have become increasingly complex in both form and function, control remains challenging for users. This has resulted in an increasing gap between user control approaches and the number of robotic motors which can be controlled. One way to address this gap is to shift some autonomy to the robot. Semi-autonomous actions of the robotic agent can then be shaped by human feedback, simplifying user control. Most prior work on agent shaping by humans has incorporated training with feedback, or has included indirect control signals. By contrast, in this paper we explore how a human can provide concurrent feedback signals and real-time myoelectric control signals to train a robot's actor-critic reinforcement learning control system. Using both a physical and a simulated robotic system, we compare training performance on a simple movement task when reward is derived from the environment, when reward is provided by the human, and combinations of these two approaches. Our results indicate that some benefit can be gained with the inclusion of human generated feedback.

Connected through hyperedge HE-1 (induced by source paper "a human centered approach to interactive machine learning"):
  with members:
    - "scalable agent alignment via reward modeling a research direction"
    - "reward learning from human preferences and demonstrations in atari"
    - "ai safety gridworlds"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "communicative capital for prosthetic agents"

Connected through hyperedge HE-2 (induced by source paper "learning user preferences via reinforcement learning with spatial interface valuing"):
  with members:
    - "evaluation of interactive machine learning systems"
    - "agent agnostic human in the loop reinforcement learning"
    - "concrete problems in ai safety"
    - "face valuing training user interfaces with facial expressions and reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "interactively shaping robot behaviour with unlabeled human instructions"):
  with members:
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "communicative capital for prosthetic agents"):
  with members:
    - "face valuing training user interfaces with facial expressions and reinforcement learning"
    - "introspective agents confidence measures for general value functions"
    - "using learned predictions as feedback to improve control and communication with an artificial limb preliminary findings"

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.HC(Human-Computer Interaction)