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: "a deep compositional framework for human like language acquisition in virtual environment"
  Abstract: Described herein are systems and methods for human-like language acquisition in a compositional framework to implement object recognition or navigation tasks. Embodiments include a method for a model to learn the input language in a grounded and compositional manner, such that after training the model is able to correctly execute zero-shot commands, which have either combination of words in the command never appeared before, and/or new object concepts learned from another task but never learned from navigation settings. In embodiments, a framework is trained end-to-end to learn simultaneously the visual representations of the environment, the syntax and semantics of the language, and outputs actions via an action module. In embodiments, the zero-shot learning capability of a framework results from its compositionality and modularity with parameter tying.

Connected through hyperedge HE-1 (induced by source paper "meta reinforcement learning with autonomous inference of subtask dependencies"):
  with members:
    - "neural logic machines"
    - "on first order meta learning algorithms"
    - "bayesian model agnostic meta learning"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "meta reinforcement learning of structured exploration strategies"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "probabilistic model agnostic meta learning"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-2 (induced by source paper "dynamic attention networks for task oriented grounding"):
  with members:
    - "probabilistic framework for solving visual dialog"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "multimodal differential network for visual question generation"
    - "differential attention for visual question answering"
    - "learning semantic sentence embeddings using sequential pair wise discriminator"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "creativity generating diverse questions using variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "task oriented language grounding for language input with multiple sub goals of non linear order"):
  with members:
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "hindsight experience replay"
    - "gated attention architectures for task oriented language grounding"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "prioritized experience replay"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"

Connected through hyperedge HE-4 (induced by source paper "talk the walk navigating new york city through grounded dialogue"):
  with members:
    - "active neural localization"
    - "unifying map and landmark based representations for visual navigation"
    - "emergent communication in a multi modal multi step referential game"
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "modulating early visual processing by language"
    - "learning visually grounded sentence representations"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parlai a dialog research software platform"

Connected through hyperedge HE-5 (induced by source paper "a new dataset and model for learning to understand navigational instructions"):
  with members:
    - "gated attention architectures for task oriented language grounding"
    - "shapeworld a new test methodology for multimodal language understanding"
    - "deep learning evaluation using deep linguistic processing"
    - "grounded language learning in a simulated 3d world"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "analyzing the behavior of visual question answering models"
    - "virtual embodiment a scalable long term strategy for artificial intelligence research"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"

Connected through hyperedge HE-6 (induced by source paper "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"):
  with members:
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "autonomous extracting a hierarchical structure of tasks in reinforcement learning and multi task reinforcement learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "neuro symbolic program synthesis"
    - "high dimensional continuous control using generalized advantage estimation"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "mazebase a sandbox for learning from games"

Connected through hyperedge HE-7 (induced by source paper "embodied question answering"):
  with members:
    - "building generalizable agents with a realistic and rich 3d environment"
    - "gated attention architectures for task oriented language grounding"
    - "deepnav learning to navigate large cities"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "tgif qa toward spatio temporal reasoning in visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "mapping instructions and visual observations to actions with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "gated attention architectures for task oriented language grounding"):
  with members:
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "target driven visual navigation in indoor scenes using deep reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "on multiplicative integration with recurrent neural networks"
    - "playing fps games with deep reinforcement learning"
    - "gated attention readers for text comprehension"

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.CL(Computation and Language)