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: "efficient programmable learning to search"
  Abstract: We improve "learning to search" approaches to structured prediction in two ways. First, we show that the search space can be defined by an arbitrary imperative program, reducing the number of lines of code required to develop new structured prediction tasks by orders of magnitude. Second, we make structured prediction orders of magnitude faster through various algorithmic improvements. We demonstrate the feasibility of our approach on three structured prediction tasks: two variants of sequence labeling and entity-relation resolution. In all cases we obtain accuracies at least as high as alternative approaches, at drastically reduced execution and programming time.

Connected through hyperedge HE-1 (induced by source paper "declarative learning based programming as an interface to ai systems"):
  with members:
    - "tensorlog deep learning meets probabilistic dbs"
    - "dynamic routing between capsules"
    - "a berkeley view of systems challenges for ai"
    - "relational learning and feature extraction by querying over heterogeneous information networks"
    - "programming with a differentiable forth interpreter"
    - "structured factored inference a framework for automated reasoning in probabilistic programming languages"
    - "tensorflow a system for large scale machine learning"
    - "the inductive constraint programming loop"

Connected through hyperedge HE-2 (induced by source paper "improving coreference resolution by learning entity level distributed representations"):
  with members:
    - "learning global features for coreference resolution"
    - "learning to search for dependencies"
    - "learning to search better than your teacher"
    - "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-3 (induced by source paper "learning reductions that really work"):
  with members:
    - "learning to search better than your teacher"
    - "logarithmic time online multiclass prediction"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "least squares revisited scalable approaches for multi class prediction"
    - "normalized online learning"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "output space search for structured prediction"
    - "online importance weight aware updates"

Connected through hyperedge HE-4 (induced by source paper "learning to search for dependencies"):
  with members:
    - "learning to search better than your teacher"
    - "reinforcement and imitation learning via interactive no regret learning"
    - "normalized online learning"
    - "output space search for structured prediction"
    - "online importance weight aware updates"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "adaptive bound optimization for online convex optimization"
    - "search based structured prediction"

Connected through hyperedge HE-5 (induced by source paper "contextual semibandits via supervised learning oracles"):
  with members:
    - "off policy evaluation for slate recommendation"
    - "efficient algorithms for adversarial contextual learning"
    - "explore no more improved high probability regret bounds for non stochastic bandits"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "contextual bandit algorithms with supervised learning guarantees"
    - "search based structured prediction"

Connected through hyperedge HE-6 (induced by source paper "parser for abstract meaning representation using learning to search"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "output space search for structured prediction"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"
    - "learning as search optimization approximate large margin methods for structured prediction"

Connected through hyperedge HE-7 (induced by source paper "searching for objects using structure in indoor scenes"):
  with members:
    - "an active search strategy for efficient object class detection"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-8 (induced by source paper "learning to search better than your teacher"):
  with members:
    - "reinforcement and imitation learning via interactive no regret learning"
    - "a reduction of imitation learning and structured prediction to no regret online learning"
    - "search based structured prediction"
    - "learning as search optimization approximate large margin methods for structured prediction"

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)