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: "webnav a new large scale task for natural language based sequential decision making"
  Abstract: We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a web site, which is represented as a graph consisting of web pages as nodes and hyperlinks as directed edges, to find a web page in which a query appears. The agent is required to have sophisticated high-level reasoning based on natural languages and efficient sequential decision making capability to succeed. We release a software tool, called WebNav, that automatically transforms a website into this goal-driven web navigation task, and as an example, we make WikiNav, a dataset constructed from the English Wikipedia containing approximately 5 million articles and more than 12 million queries for training. We evaluate two different agents based on neural networks on the WikiNav and provide the human performance. Our results show the difficulty of the task for both humans and machines. With this benchmark, we expect faster progress in developing artificial agents with natural language understanding and planning skills.

Connected through hyperedge HE-1 (induced by source paper "constructing datasets for multi hop reading comprehension across documents"):
  with members:
    - "cross sentence n ary relation extraction with graph lstms"
    - "triviaqa a large scale distantly supervised challenge dataset for reading comprehension"
    - "neural question answering at bioasq 5b"
    - "searchqa a new q a dataset augmented with context from a search engine"
    - "the effect of different writing tasks on linguistic style a case study of the roc story cloze task"
    - "end to end differentiable proving"
    - "zero shot relation extraction via reading comprehension"
    - "making neural qa as simple as possible but not simpler"

Connected through hyperedge HE-2 (induced by source paper "reasonet learning to stop reading in machine comprehension"):
  with members:
    - "attention over attention neural networks for reading comprehension"
    - "learning recurrent span representations for extractive question answering"
    - "exploring question understanding and adaptation in neural network based question answering"
    - "reading wikipedia to answer open domain questions"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "bidirectional attention flow for machine comprehension"
    - "text understanding with the attention sum reader network"
    - "iterative alternating neural attention for machine reading"

Connected through hyperedge HE-3 (induced by source paper "value iteration networks"):
  with members:
    - "deep learning for reward design to improve monte carlo tree search in atari games"
    - "asynchronous methods for deep reinforcement learning"
    - "end to end goal driven web navigation"
    - "benchmarking deep reinforcement learning for continuous control"
    - "learning simple algorithms from examples"
    - "robobarista object part based transfer of manipulation trajectories from crowd sourcing in 3d pointclouds"
    - "spatial transformer networks"
    - "embed to control a locally linear latent dynamics model for control from raw images"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning with a natural language action space"):
  with members:
    - "end to end goal driven web navigation"
    - "effective approaches to attention based neural machine translation"
    - "skip thought vectors"
    - "continuous control with deep reinforcement learning"
    - "language understanding for text based games using deep reinforcement learning"
    - "learning to win by reading manuals in a monte carlo framework"
    - "distributed representations of sentences and documents"
    - "sequence to sequence learning with 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)