human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning calibratable policies using programmatic style consistency"):
  Abstract: We study the important and challenging problem of controllable generation of long-term sequential behaviors. Solutions to this problem would impact many applications, such as calibrating behaviors of AI agents in games or predicting player trajectories in sports. In contrast to the well-studied areas of controllable generation of images, text, and speech, there are significant challenges that are unique to or exacerbated by generating long-term behaviors: how should we specify the factors of variation to control, and how can we ensure that the generated temporal behavior faithfully demonstrates diverse styles? In this paper, we leverage large amounts of raw behavioral data to learn policies that can be calibrated to generate a diverse range of behavior styles (e.g., aggressive versus passive play in sports). Inspired by recent work on leveraging programmatic labeling functions, we present a novel framework that combines imitation learning with data programming to learn style-calibratable policies. Our primary technical contribution is a formal notion of style-consistency as a learning objective, and its integration with conventional imitation learning approaches. We evaluate our framework using demonstrations from professional basketball players and agents in the MuJoCo physics environment, and show that our learned policies can be accurately calibrated to generate interesting behavior styles in both domains.

Includes members:
  Member "deepmind control suite":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "algorithmic framework for model based deep reinforcement learning with theoretical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "adversarial information factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local facial attribute transfer through inpainting")
  Member "isolating sources of disentanglement in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "learning latent subspaces in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "generating multi agent trajectories using programmatic weak supervision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "panda a gigapixel level human centric video dataset")

Question: Please tell me which class the source paper 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)