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: "search based structured prediction"
  Abstract: We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.

Connected through hyperedge HE-1 (induced by source paper "loss annealed gail for sample efficient and stable imitation learning"):
  with members:
    - "on the difficulty of warm starting neural network training"
    - "rethinking imagenet pre training"
    - "addressing function approximation error in actor critic methods"
    - "learning goal embeddings via self play for hierarchical reinforcement learning"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-2 (induced by source paper "correlated adversarial imitation learning"):
  with members:
    - "causal confusion in imitation learning"
    - "maximum causal tsallis entropy imitation learning"
    - "quantifying generalization in reinforcement learning"
    - "learning in non convex games with an optimization oracle"
    - "on convergence and stability of gans"
    - "an online learning approach to generative adversarial networks"
    - "efficient regret minimization in non convex games"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-3 (induced by source paper "mpc guided imitation learning of neural network policies for the artificial pancreas"):
  with members:
    - "pac confidence sets for deep neural networks via calibrated prediction"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "safe end to end imitation learning for model predictive control"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "dropoutdagger a bayesian approach to safe imitation learning"
    - "data driven robust control for type 1 diabetes under meal and exercise uncertainties"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "human ai interaction loop training new approach for interactive reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "deep q learning from demonstrations"
    - "generative adversarial imitation learning"
    - "asynchronous methods for deep reinforcement learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-5 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "asking clarifying questions in open domain information seeking conversations"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"

Connected through hyperedge HE-6 (induced by source paper "non monotonic sequential text generation"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "relational inductive biases deep learning and graph networks"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "texygen a benchmarking platform for text generation models"
    - "language gans falling short"
    - "the importance of generation order in language modeling"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-7 (induced by source paper "help anna visual navigation with natural multimodal assistance via retrospective curiosity encouraging imitation learning"):
  with members:
    - "vision and dialog navigation"
    - "rerere remote embodied referring expressions in real indoor environments"
    - "embodied question answering in photorealistic environments with point cloud perception"
    - "from video game to real robot the transfer between action spaces"
    - "learning to speak and act in a fantasy text adventure game"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "neural text generation with unlikelihood training"
    - "the regretful agent heuristic aided navigation through progress estimation"

Connected through hyperedge HE-8 (induced by source paper "regressing word and sentence embeddings for regularization of neural machine translation"):
  with members:
    - "rewe regressing word embeddings for regularization of neural machine translation systems"
    - "pre trained language model representations for language generation"
    - "differentiable sampling with flexible reference word order for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "training neural machine translation using word embedding based loss"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "deep contextualized word representations"

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.LG(Machine Learning)