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: "scalable agent alignment via reward modeling a research direction"
  Abstract: One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user's intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.

Connected through hyperedge HE-1 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "a survey on bias and fairness in machine learning"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "social and governance implications of improved data efficiency"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the offense defense balance of scientific knowledge does publishing ai research reduce misuse"
    - "few shot adversarial learning of realistic neural talking head models"
    - "data efficient image recognition with contrastive predictive coding"
    - "beyond human level accuracy computational challenges in deep learning"
    - "impact of training dataset size on neural answer selection models"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "incentivizing the emergence of grounded discrete communication between general agents"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of linguistic communication from referential games with symbolic and pixel input"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "natural language does not emerge naturally in multi agent dialog"
    - "emergence of grounded compositional language in multi agent populations"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "artificial intelligence values and alignment"):
  with members:
    - "agi safety literature review"
    - "incomplete contracting and ai alignment"
    - "machine theory of mind"
    - "impossibility and uncertainty theorems in ai value alignment or why your agi should not have a utility function"
    - "constrained policy optimization"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-5 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-6 (induced by source paper "fine tuning language models from human preferences"):
  with members:
    - "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"
    - "finding generalizable evidence by learning to convince q a models"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "sample efficient text summarization using a single pre trained transformer"
    - "towards coherent and cohesive long form text generation"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"
    - "learning from dialogue after deployment feed yourself chatbot"

Connected through hyperedge HE-7 (induced by source paper "relational mimic for visual adversarial imitation learning"):
  with members:
    - "tactilegcn a graph convolutional network for predicting grasp stability with tactile sensors"
    - "stand alone self attention in vision models"
    - "the gan landscape losses architectures regularization and normalization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "two stream adaptive graph convolutional networks for skeleton based action recognition"
    - "generative adversarial imitation from observation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"):
  with members:
    - "categorizing wireheading in partially embedded agents"
    - "model based reinforcement learning for atari"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "modeling agi safety frameworks with causal influence diagrams"
    - "neural predictive belief representations"
    - "agi safety literature review"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

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)