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: "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
  Abstract: Traditional exploration methods in RL require agents to perform random actions to find rewards. But these approaches struggle on sparse-reward domains like Montezuma's Revenge where the probability that any random action sequence leads to reward is extremely low. Recent algorithms have performed well on such tasks by encouraging agents to visit new states or perform new actions in relation to all prior training episodes (which we call across-training novelty). But such algorithms do not consider whether an agent exhibits intra-life novelty: doing something new within the current episode, regardless of whether those behaviors have been performed in previous episodes. We hypothesize that across-training novelty might discourage agents from revisiting initially non-rewarding states that could become important stepping stones later in training. We introduce Deep Curiosity Search (DeepCS), which encourages intra-life exploration by rewarding agents for visiting as many different states as possible within each episode, and show that DeepCS matches the performance of current state-of-the-art methods on Montezuma's Revenge. We further show that DeepCS improves exploration on Amidar, Freeway, Gravitar, and Tutankham (many of which are hard exploration games). Surprisingly, DeepCS doubles A2C performance on Seaquest, a game we would not have expected to benefit from intra-life exploration because the arena is small and already easily navigated by naive exploration techniques. In one run, DeepCS achieves a maximum training score of 80,000 points on Seaquest, higher than any methods other than Ape-X. The strong performance of DeepCS on these sparse- and dense-reward tasks suggests that encouraging intra-life novelty is an interesting, new approach for improving performance in Deep RL and motivates further research into hybridizing across-training and intra-life exploration methods.

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"
    - "torchbeast a pytorch platform for distributed rl"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "never give up learning directed exploration strategies"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "mulex disentangling exploitation from exploration in deep rl"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "meta gradient reinforcement learning"
    - "an introduction to deep reinforcement learning"
    - "large scale study of curiosity driven learning"

Connected through hyperedge HE-3 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "self imitation learning via trajectory conditioned policy for hard exploration tasks"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "behaviour suite for reinforcement learning"
    - "skew fit state covering self supervised reinforcement learning"
    - "making efficient use of demonstrations to solve hard exploration problems"
    - "generative adversarial self imitation learning"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "zero shot visual imitation"

Connected through hyperedge HE-5 (induced by source paper "exploration by random network distillation"):
  with members:
    - "count based exploration with the successor representation"
    - "temporal difference models model free deep rl for model based control"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "playing hard exploration games by watching youtube"
    - "expert augmented actor critic for vizdoom and montezumas revenge"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "self imitation learning"

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)