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: "source target inference models for spatial instruction understanding"
  Abstract: Models that can execute natural language instructions for situated robotic tasks such as assembly and navigation have several useful applications in homes, offices, and remote scenarios. We study the semantics of spatially-referred configuration and arrangement instructions, based on the challenging Bisk-2016 blank-labeled block dataset. This task involves finding a source block and moving it to the target position (mentioned via a reference block and offset), where the blocks have no names or colors and are just referred to via spatial location features. We present novel models for the subtasks of source block classification and target position regression, based on joint-loss language and spatial-world representation learning, as well as CNN-based and dual attention models to compute the alignment between the world blocks and the instruction phrases. For target position prediction, we compare two inference approaches: annealed sampling via policy gradient versus expectation inference via supervised regression. Our models achieve the new state-of-the-art on this task, with an improvement of 47% on source block accuracy and 22% on target position distance.

Connected through hyperedge HE-1 (induced by source paper "improving natural language interaction with robots using advice"):
  with members:
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "a network based end to end trainable task oriented dialogue system"
    - "adam a method for stochastic optimization"
    - "on the difficulty of training recurrent neural networks"
    - "a joint model of language and perception for grounded attribute learning"

Connected through hyperedge HE-2 (induced by source paper "value based search in execution space for mapping instructions to programs"):
  with members:
    - "reinforcewalk learning to walk in graph with monte carlo tree search"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "situated mapping of sequential instructions to actions with single step reward observation"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "breaking the beam search curse a study of re scoring methods and stopping criteria for neural machine translation"
    - "representation learning for grounded spatial reasoning"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"

Connected through hyperedge HE-3 (induced by source paper "situated mapping of sequential instructions to actions with single step reward observation"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "from language to programs bridging reinforcement learning and maximum marginal likelihood"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "learning interpretable spatial operations in a rich 3d blocks world"
    - "neural symbolic machines learning semantic parsers on freebase with weak supervision"
    - "simpler context dependent logical forms via model projections"
    - "listen attend and walk neural mapping of navigational instructions to action sequences"
    - "learning to search better than your teacher"

Connected through hyperedge HE-4 (induced by source paper "learning interpretable spatial operations in a rich 3d blocks world"):
  with members:
    - "inferring and executing programs for visual reasoning"
    - "naturalizing a programming language via interactive learning"
    - "a simple neural network module for relational reasoning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "learning language games through interaction"
    - "learning to compose neural networks for question answering"
    - "generation and comprehension of unambiguous object descriptions"
    - "vqa visual question answering"

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)