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: "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
  Abstract: Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, it would significantly improve their ability to understand our intent and to transfer tasks between different environments. To that end, we introduce a computational framework that replicates aspects of human concept learning. Concepts are represented as programs on a novel computer architecture consisting of a visual perception system, working memory, and action controller. The instruction set of this "cognitive computer" has commands for parsing a visual scene, directing gaze and attention, imagining new objects, manipulating the contents of a visual working memory, and controlling arm movement. Inferring a concept corresponds to inducing a program that can transform the input to the output. Some concepts require the use of imagination and recursion. Previously learned concepts simplify the learning of subsequent more elaborate concepts, and create a hierarchy of abstractions. We demonstrate how a robot can use these abstractions to interpret novel concepts presented to it as schematic images, and then apply those concepts in dramatically different situations. By bringing cognitive science ideas on mental imagery, perceptual symbols, embodied cognition, and deictic mechanisms into the realm of machine learning, our work brings us closer to the goal of building robots that have interpretable representations and commonsense.

Connected through hyperedge HE-1 (induced by source paper "a model of fast concept inference with object factorized cognitive programs"):
  with members:
    - "reward learning from narrated demonstrations"

Connected through hyperedge HE-2 (induced by source paper "unsupervised doodling and painting with improved spiral"):
  with members:
    - "photo sketching inferring contour drawings from images"
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "meta sim learning to generate synthetic datasets"
    - "pragmatic inference and visual abstraction enable contextual flexibility during visual communication"
    - "generating diverse programs with instruction conditioned reinforced adversarial learning"
    - "can computers create art"
    - "learning to search with mctsnets"

Connected through hyperedge HE-3 (induced by source paper "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "learning powerful policies by using consistent dynamics model"
    - "mega reward achieving human level play without extrinsic rewards"
    - "model based reinforcement learning for atari"
    - "strategic object oriented reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-4 (induced by source paper "motion reasoning for goal based imitation learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"
    - "scaling egocentric vision the epic kitchens dataset"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "rmpflow a computational graph for automatic motion policy generation"

Connected through hyperedge HE-5 (induced by source paper "from explanation to synthesis compositional program induction for learning from demonstration"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "specification patterns for robotic missions"
    - "learning programmatically structured representations with perceptor gradients"
    - "deep drone racing learning agile flight in dynamic environments"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "programmatically interpretable reinforcement learning"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "verifiable reinforcement learning via policy extraction"

Connected through hyperedge HE-6 (induced by source paper "relation learning in a neurocomputational architecture supports cross domain transfer"):
  with members:
    - "relational inductive biases deep learning and graph networks"
    - "object detection with deep learning a review"
    - "relational inductive bias for physical construction in humans and machines"
    - "mask r cnn"
    - "a persona based neural conversation model"
    - "overcoming catastrophic forgetting in neural networks"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards deep symbolic reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "using machine learning to guide cognitive modeling a case study in moral reasoning"):
  with members:
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "how deep is knowledge tracing"

Connected through hyperedge HE-8 (induced by source paper "human like generalization in a machine through predicate learning"):
  with members:
    - "innateness alphazero and artificial intelligence"
    - "deep learning a critical appraisal"
    - "overcoming catastrophic forgetting in neural networks"
    - "towards deep symbolic reinforcement learning"
    - "building machines that learn and think like people"
    - "deep learning in 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.AI(Artificial Intelligence)