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: "pragmatic pedagogic value alignment"
  Abstract: As intelligent systems gain autonomy and capability, it becomes vital to ensure that their objectives match those of their human users; this is known as the value-alignment problem. In robotics, value alignment is key to the design of collaborative robots that can integrate into human workflows, successfully inferring and adapting to their users’ objectives as they go. We argue that a meaningful solution to value alignment must combine multi-agent decision theory with rich mathematical models of human cognition, enabling robots to tap into people’s natural collaborative capabilities. We present a solution to the cooperative inverse reinforcement learning (CIRL) dynamic game based on well-established cognitive models of decision making and theory of mind. The solution captures a key reciprocity relation: the human will not plan her actions in isolation, but rather reason pedagogically about how the robot might learn from them; the robot, in turn, can anticipate this and interpret the human’s actions pragmatically. To our knowledge, this work constitutes the first formal analysis of value alignment grounded in empirically validated cognitive models.

Connected through hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences"):
  with members:
    - "asking easy questions a user friendly approach to active reward learning"
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "functional variational bayesian neural networks"
    - "causal confusion in imitation learning"
    - "a divergence minimization perspective on imitation learning methods"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"

Connected through hyperedge HE-2 (induced by source paper "quantifying hypothesis space misspecification in learning from human robot demonstrations and physical corrections"):
  with members:
    - "autonomous interpretation of demonstrations for modification of dynamical movement primitives"
    - "risk aware active inverse reinforcement learning"
    - "an algorithmic perspective on imitation learning"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "including uncertainty when learning from human corrections"
    - "learning under misspecified objective spaces"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "trajectory deformations from physical human robot interaction"

Connected through hyperedge HE-3 (induced by source paper "emergence of pragmatics from referential game between theory of mind agents"):
  with members:
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "compositional obverter communication learning from raw visual input"
    - "planning inference and pragmatics in sequential language games"
    - "emergence of communication in an interactive world with consistent speakers"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergent communication through negotiation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-4 (induced by source paper "sequential cooperative bayesian inference"):
  with members:
    - "a mathematical theory of cooperative communication"
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "cooperative inverse reinforcement learning"
    - "infant directed speech is consistent with teaching"
    - "machine teaching for bayesian learners in the exponential family"

Connected through hyperedge HE-5 (induced by source paper "literal or pedagogic human analyzing human model misspecification in objective learning"):
  with members:
    - "an efficient generalized bellman update for cooperative inverse reinforcement learning"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-6 (induced by source paper "a mathematical theory of cooperative communication"):
  with members:
    - "generalizing the theory of cooperative inference"
    - "optimal cooperative inference"
    - "toward a general scaleable framework for bayesian teaching with applications to topic models"
    - "infant directed speech is consistent with teaching"
    - "optimal transport for domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "verification for machine learning autonomy and neural networks survey"):
  with members:
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deep reinforcement learning for autonomous driving"
    - "a dual approach to scalable verification of deep networks"
    - "neural guided deductive search for real time program synthesis from examples"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "reachable set estimation and verification for neural network models of nonlinear dynamic systems"
    - "testing deep neural networks"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-8 (induced by source paper "modeling others using oneself in multi agent reinforcement learning"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "inverse reward design"
    - "multi agent actor critic for mixed cooperative competitive environments"

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)