human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "value based search in execution space for mapping instructions to programs"):
  Abstract: Training models to map natural language instructions to programs given target world supervision only requires searching for good programs at training time. Search is commonly done using beam search in the space of partial programs or program trees, but as the length of the instructions grows finding a good program becomes difficult. In this work, we propose a search algorithm that uses the target world state, known at training time, to train a critic network that predicts the expected reward of every search state. We then score search states on the beam by interpolating their expected reward with the likelihood of programs represented by the search state. Moreover, we search not in the space of programs but in a more compressed state of program executions, augmented with recent entities and actions. On the SCONE dataset, we show that our algorithm dramatically improves performance on all three domains compared to standard beam search and other baselines.

Includes members:
  Member "reinforcewalk learning to walk in graph with monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")
  Member "policy shaping and generalized update equations for semantic parsing from denotations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scalable neural methods for reasoning with a symbolic knowledge base")
  Member "situated mapping of sequential instructions to actions with single step reward observation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "editing based sql query generation for cross domain context dependent questions")
  Member "mapping instructions to actions in 3d environments with visual goal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "retouchdown adding touchdown to streetlearn as a shareable resource for language grounding tasks in street view")
  Member "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "break it down a question understanding benchmark")
  Member "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "pseudolikelihood reranking with masked language models")
  Member "representation learning for grounded spatial reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "following instructions by imagining and reaching visual goals")
  Member "from language to programs bridging reinforcement learning and maximum marginal likelihood":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "why build an assistant in minecraft")

Question: Please tell me which class the source paper 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)