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 "embodied multimodal multitask learning"):
  Abstract: Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for different multimodal tasks, such as semantic goal navigation and embodied question answering. In this paper, we propose a multitask model capable of jointly learning these multimodal tasks, and transferring knowledge of words and their grounding in visual objects across the tasks. The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual concepts in the visual representations, and align them with each other. This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks. We show that the proposed model outperforms a range of baselines on both tasks in simulated 3D environments. We also show that this disentanglement of representations makes our model modular, interpretable, and allows for transfer to instructions containing new words by leveraging object detectors.

Includes members:
  Member "guided feature transformation gft a neural language grounding module for embodied agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self monitoring navigation agent via auxiliary progress estimation")
  Member "building generalizable agents with a realistic and rich 3d environment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "compositional attention networks for machine reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unshuffling data for improved generalization")
  Member "the sound of pixels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the state of service robots current bottlenecks in object perception and manipulation")
  Member "interactive grounded language acquisition and generalization in a 2d world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning")
  Member "minos multimodal indoor simulator for navigation in complex environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "citylearn diverse real world environments for sample efficient navigation policy learning")
  Member "gated attention architectures for task oriented language grounding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "language as a cognitive tool to imagine goals in curiosity driven exploration")
  Member "zero shot task generalization with multi task deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on catastrophic interference in atari 2600 games")

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)