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: "where do you think you re going inferring beliefs about dynamics from behavior"
  Abstract: Inferring intent from observed behavior has been studied extensively within the frameworks of Bayesian inverse planning and inverse reinforcement learning. These methods infer a goal or reward function that best explains the actions of the observed agent, typically a human demonstrator. Another agent can use this inferred intent to predict, imitate, or assist the human user. However, a central assumption in inverse reinforcement learning is that the demonstrator is close to optimal. While models of suboptimal behavior exist, they typically assume that suboptimal actions are the result of some type of random noise or a known cognitive bias, like temporal inconsistency. In this paper, we take an alternative approach, and model suboptimal behavior as the result of internal model misspecification: the reason that user actions might deviate from near-optimal actions is that the user has an incorrect set of beliefs about the rules -- the dynamics -- governing how actions affect the environment. Our insight is that while demonstrated actions may be suboptimal in the real world, they may actually be near-optimal with respect to the user's internal model of the dynamics. By estimating these internal beliefs from observed behavior, we arrive at a new method for inferring intent. We demonstrate in simulation and in a user study with 12 participants that this approach enables us to more accurately model human intent, and can be used in a variety of applications, including offering assistance in a shared autonomy framework and inferring human preferences.

Connected through hyperedge HE-1 (induced by source paper "don t feed the troll detecting troll behavior via inverse reinforcement learning"):
  with members:
    - "analysing user identity via time sensitive semantic edit distance t sed a case study of russian trolls on twitter"
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "red bots do it better comparative analysis of social bot partisan behavior"
    - "arming the public with artificial intelligence to counter social bots"
    - "senator we sell ads analysis of the 2016 russian facebook ads campaign"
    - "characterizing the 2016 russian ira influence campaign"
    - "who let the trolls out towards understanding state sponsored trolls"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"

Connected through hyperedge HE-2 (induced by source paper "sqil imitation learning via reinforcement learning with sparse rewards"):
  with members:
    - "random expert distillation imitation learning via expert policy support estimation"
    - "reinforcement learning from imperfect demonstrations"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the gan landscape losses architectures regularization and normalization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "recurrent world models facilitate policy evolution"
    - "deep reinforcement learning that matters"

Connected through hyperedge HE-3 (induced by source paper "building second order mental models for human robot interaction"):
  with members:
    - "on the utility of model learning in hri"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modelling user s theory of ai s mind in interactive intelligent systems"
    - "machine theory of mind"
    - "towards efficient detection and optimal response against sophisticated opponents"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "enabling robots to communicate their objectives"

Connected through hyperedge HE-4 (induced by source paper "on the feasibility of learning rather than assuming human biases for reward inference"):
  with members:
    - "universal planning networks"
    - "multi task maximum entropy inverse reinforcement learning"
    - "learning to search with mctsnets"
    - "learning a prior over intent via meta inverse reinforcement learning"
    - "machine theory of mind"
    - "learning model based planning from scratch"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "inverse reward design"

Connected through hyperedge HE-5 (induced by source paper "online explanation generation for human robot teaming"):
  with members:
    - "progressive explanation generation for human robot teaming"
    - "explanation in artificial intelligence insights from the social sciences"
    - "ai challenges in human robot cognitive teaming"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "explicablility as minimizing distance from expected behavior"
    - "plan explicability and predictability for robot task planning"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-6 (induced by source paper "inverse rational control with partially observable continuous nonlinear dynamics"):
  with members:
    - "inverse pomdp inferring what you think from what you do"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "inverse reinforcement learning with simultaneous estimation of rewards and dynamics"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "model free model reconciliation"):
  with members:
    - "explanation in artificial intelligence insights from the social sciences"
    - "plan explanations as model reconciliation moving beyond explanation as soliloquy"
    - "why should i trust you explaining the predictions of any classifier"
    - "plan explicability and predictability for robot task planning"

Connected through hyperedge HE-8 (induced by source paper "personalized apprenticeship learning from heterogeneous decision makers"):
  with members:
    - "starcraft ii a new challenge for reinforcement learning"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"

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)