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: "learning to search for dependencies"
  Abstract: We demonstrate that a dependency parser can be built using a credit assignment compiler which removes the burden of worrying about low-level machine learning details from the parser implementation. The result is a simple parser which robustly applies to many languages that provides similar statistical and computational performance with best-to-date transition-based parsing approaches, while avoiding various downsides including randomization, extra feature requirements, and custom learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "provably efficient imitation learning from observation alone"):
  with members:
    - "information theoretic considerations in batch reinforcement learning"
    - "imitating latent policies from observation"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "model based rl in contextual decision processes pac bounds and exponential improvements over model free approaches"
    - "behavioral cloning from observation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

Connected through hyperedge HE-2 (induced by source paper "a neural entity coreference resolution review"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "incorporating context and external knowledge for pronoun coreference resolution"
    - "gendered ambiguous pronouns shared task boosting model confidence by evidence pooling"
    - "knowledge aware pronoun coreference resolution"
    - "measuring bias in contextualized word representations"
    - "improving generalization in coreference resolution via adversarial training"
    - "look again at the syntax relational graph convolutional network for gendered ambiguous pronoun resolution"
    - "gender bias in contextualized word embeddings"

Connected through hyperedge HE-3 (induced by source paper "deeply aggrevated differentiable imitation learning for sequential prediction"):
  with members:
    - "adaptive information gathering via imitation learning"
    - "deep reinforcement learning for dialogue generation"
    - "generative adversarial imitation learning"
    - "an actor critic algorithm for sequence prediction"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "plato policy learning using adaptive trajectory optimization"
    - "benchmarking deep reinforcement learning for continuous control"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "book storing algorithm invariant episodes for deep reinforcement learning"):
  with members:
    - "neural episodic control"
    - "asynchronous methods for deep reinforcement learning"
    - "prioritized experience replay"
    - "trust region policy optimization"
    - "dueling network architectures for deep reinforcement learning"
    - "learning to search better than your teacher"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "the umd neural machine translation systems at wmt17 bandit learning task"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "asynchronous methods for deep reinforcement learning"
    - "stochastic structured prediction under bandit feedback"
    - "neural machine translation of rare words with subword units"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-6 (induced by source paper "improving coreference resolution by learning entity level distributed representations"):
  with members:
    - "learning global features for coreference resolution"
    - "learning to search better than your teacher"
    - "efficient programmable learning to search"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "narrowing the modeling gap a cluster ranking approach to coreference resolution"
    - "search based structured prediction"

Connected through hyperedge HE-7 (induced by source paper "efficient programmable learning to search"):
  with members:
    - "transition based dependency parsing with stack long short term memory"
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "pegasus a policy search method for large mdps and pomdps"
    - "normalized online learning"
    - "church a language for generative models"
    - "output space search for structured prediction"
    - "a reliable effective terascale linear learning system"

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)