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: "model free model based and general intelligence"
  Abstract: During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

Connected through hyperedge HE-1 (induced by source paper "textual analysis of artificial intelligence manuscripts reveals features associated with peer review outcome"):
  with members:
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (induced by source paper "a review on intelligent object perception methods combining knowledge based reasoning and machine learning"):
  with members:
    - "what object should i use task driven object detection"
    - "scene graph generation with external knowledge and image reconstruction"
    - "robocse robot common sense embedding"
    - "recent advances in imitation learning from observation"
    - "integrating knowledge and reasoning in image understanding"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-3 (induced by source paper "a boxology of design patterns for hybrid learning and reasoning systems"):
  with members:
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "reasoning over rdf knowledge bases using deep learning"
    - "relational inductive biases deep learning and graph networks"
    - "deep learning a critical appraisal"
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "knowledge based transfer learning explanation"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-4 (induced by source paper "learning first order symbolic representations for planning from the structure of the state space"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "combined reinforcement learning via abstract representations"
    - "transfer of deep reactive policies for mdp planning"
    - "learning features and abstract actions for computing generalized plans"
    - "deep learning a critical appraisal"
    - "human level intelligence or animal like abilities"

Connected through hyperedge HE-5 (induced by source paper "on hard exploration for reinforcement learning a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "continual match based training in pommerman technical report"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "backplay man muss immer umkehren"
    - "using monte carlo tree search as a demonstrator within asynchronous deep rl"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "human level intelligence or animal like abilities"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (induced by source paper "comparing knowledge based reinforcement learning to neural networks in a strategy game"):
  with members:
    - "playing a strategy game with knowledge based reinforcement learning"
    - "explaining explanations an overview of interpretability of machine learning"
    - "evolving simple programs for playing atari games"
    - "deep reinforcement learning with double q learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "intelligent techniques for resolving conflicts of knowledge in multi agent decision support systems"

Connected through hyperedge HE-7 (induced by source paper "asnets deep learning for generalised planning"):
  with members:
    - "generalized value iteration networks life beyond lattices"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "the mythos of model interpretability"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "content and linguistic biases in the peer review process of artificial intelligence conferences"):
  with members:
    - "a dataset of peer reviews peerread collection insights and nlp applications"

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)