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: "reverse curriculum generation for reinforcement learning"
  Abstract: Many relevant tasks require an agent to reach a certain state, or to manipulate objects into a desired configuration. For example, we might want a robot to align and assemble a gear onto an axle or insert and turn a key in a lock. These goal-oriented tasks present a considerable challenge for reinforcement learning, since their natural reward function is sparse and prohibitive amounts of exploration are required to reach the goal and receive some learning signal. Past approaches tackle these problems by exploiting expert demonstrations or by manually designing a task-specific reward shaping function to guide the learning agent. Instead, we propose a method to learn these tasks without requiring any prior knowledge other than obtaining a single state in which the task is achieved. The robot is trained in reverse, gradually learning to reach the goal from a set of start states increasingly far from the goal. Our method automatically generates a curriculum of start states that adapts to the agent's performance, leading to efficient training on goal-oriented tasks. We demonstrate our approach on difficult simulated navigation and fine-grained manipulation problems, not solvable by state-of-the-art reinforcement learning methods.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "curriculum by texture"):
  with members:
    - "do better imagenet models transfer better"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "simple baselines for human pose estimation and tracking"
    - "automated curriculum learning for neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "cycada cycle consistent adversarial domain adaptation"
    - "teacher student curriculum learning"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "enhanced poet open ended reinforcement learning through unbounded invention of learning challenges and their solutions"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "go explore a new approach for hard exploration problems"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "alphastar an evolutionary computation perspective"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "automatic goal generation for reinforcement learning agents"
    - "teacher student curriculum learning"

Connected through hyperedge HE-4 (induced by source paper "curriculum in gradient based meta reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "mame model agnostic meta exploration"
    - "learning domain randomization distributions for transfer of locomotion policies"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-5 (induced by source paper "exploiting language instructions for interpretable and compositional reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "learning to understand goal specifications by modelling reward"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "visualisation and diagnostic classifiers reveal how recurrent and recursive neural networks process hierarchical structure"
    - "visualizing and understanding atari agents"

Connected through hyperedge HE-6 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dota 2 with large scale deep reinforcement learning"
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "adversarial attacks on neural network policies"
    - "automatic goal generation for reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for complex manipulation tasks with sparse feedback"):
  with members:
    - "bias reduced hindsight experience replay with virtual goal prioritization"
    - "exploration by random network distillation"
    - "learning curriculum policies for reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "energy based hindsight experience prioritization"
    - "learning montezuma s revenge from a single demonstration"
    - "hindsight experience replay"
    - "automatic goal generation for reinforcement learning agents"

Connected through hyperedge HE-8 (induced by source paper "reinforcement co learning of deep and spiking neural networks for energy efficient mapless navigation with neuromorphic hardware"):
  with members:
    - "spiking neural network on neuromorphic hardware for energy efficient unidimensional slam"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "parameter space noise for exploration"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"

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.AI(Artificial Intelligence)