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: "accuracy based curriculum learning in deep reinforcement learning"
  Abstract: In this paper, we investigate a new form of automated curriculum learning based on adaptive selection of accuracy requirements, called accuracy-based curriculum learning. Using a reinforcement learning agent based on the Deep Deterministic Policy Gradient algorithm and addressing the Reacher environment, we first show that an agent trained with various accuracy requirements sampled randomly learns more efficiently than when asked to be very accurate at all times. Then we show that adaptive selection of accuracy requirements, based on a local measure of competence progress, automatically generates a curriculum where difficulty progressively increases, resulting in a better learning efficiency than sampling randomly.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "accelerating reinforcement learning for reaching using continuous curriculum learning"):
  with members:
    - "harnessing reinforcement learning for neural motion planning"
    - "interactive open ended object affordance and grasp learning for robotic manipulation"
    - "deep learning using rectified linear units relu"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "benchmarking reinforcement learning algorithms on real world robots"
    - "hindsight experience replay"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "optlayer practical constrained optimization for deep reinforcement learning in the real world"

Connected through hyperedge HE-3 (induced by source paper "curiosity driven multi criteria hindsight experience replay"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "exploration by random network distillation"
    - "improvements on hindsight learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven experience prioritization via density estimation"
    - "multi objective model based policy search for data efficient learning with sparse rewards"

Connected through hyperedge HE-4 (induced by source paper "efficient reinforcement learning with a thought game for starcraft"):
  with members:
    - "relational deep reinforcement learning"
    - "macro action selection with deep reinforcement learning in starcraft"
    - "world models"
    - "hierarchical macro strategy model for moba game ai"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"
    - "modular architecture for starcraft ii with deep reinforcement learning"
    - "tunneling neural perception and logic reasoning through abductive learning"

Connected through hyperedge HE-5 (induced by source paper "self paced contextual reinforcement learning"):
  with members:
    - "temporal difference models model free deep rl for model based control"
    - "computational theories of curiosity driven learning"
    - "learning curriculum policies for reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"
    - "reverse curriculum generation for reinforcement learning"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "curriculum goal masking for continuous deep reinforcement learning"):
  with members:
    - "from semantics to execution integrating action planning with reinforcement learning for robotic tool use"
    - "learning dexterous in hand manipulation"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"
    - "teacher student curriculum learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "scheduled sampling for sequence prediction with recurrent neural networks"

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)