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: "torchbeast a pytorch platform for distributed rl"
  Abstract: TorchBeast is a platform for reinforcement learning (RL) research in PyTorch. It implements a version of the popular IMPALA algorithm for fast, asynchronous, parallel training of RL agents. Additionally, TorchBeast has simplicity as an explicit design goal: We provide both a pure-Python implementation ("MonoBeast") as well as a multi-machine high-performance version ("PolyBeast"). In the latter, parts of the implementation are written in C++, but all parts pertaining to machine learning are kept in simple Python using PyTorch, with the environments provided using the OpenAI Gym interface. This enables researchers to conduct scalable RL research using TorchBeast without any programming knowledge beyond Python and PyTorch. In this paper, we describe the TorchBeast design principles and implementation and demonstrate that it performs on-par with IMPALA on Atari. TorchBeast is released as an open-source package under the Apache 2.0 license and is available at \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "ride rewarding impact driven exploration for procedurally generated environments"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "scheduled intrinsic drive a hierarchical take on intrinsically motivated exploration"
    - "craftassist instruction parsing semantic parsing for a minecraft assistant"
    - "unsupervised discovery of decision states for transfer in reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "count based exploration with the successor representation"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-2 (induced by source paper "seed rl scalable and efficient deep rl with accelerated central inference"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "options as responses grounding behavioural hierarchies in multi agent rl"
    - "discovery of useful questions as auxiliary tasks"
    - "google research football a novel reinforcement learning environment"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "cross view policy learning for street navigation"
    - "neural replicator dynamics"
    - "fast task inference with variational intrinsic successor features"

Connected through hyperedge HE-3 (induced by source paper "rtfm generalising to novel environment dynamics via reading"):
  with members:
    - "language as an abstraction for hierarchical deep reinforcement learning"
    - "a survey of reinforcement learning informed by natural language"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "speaker follower models for vision and language navigation"
    - "global locally self attentive dialogue state tracker"
    - "guiding policies with language via meta learning"
    - "end to end neural coreference resolution"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "mvfst rl an asynchronous rl framework for congestion control with delayed actions"):
  with members:
    - "iroko a framework to prototype reinforcement learning for data center traffic control"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

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)