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: "alignment based compositional semantics for instruction following"
  Abstract: This paper describes an alignment-based model for interpreting natural language instructions in context. We approach instruction following as a search over plans, scoring sequences of actions conditioned on structured observations of text and the environment. By explicitly modeling both the low-level compositional structure of individual actions and the high-level structure of full plans, we are able to learn both grounded representations of sentence meaning and pragmatic constraints on interpretation. To demonstrate the model's flexibility, we apply it to a diverse set of benchmark tasks. On every task, we outperform strong task-specific baselines, and achieve several new state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "on the interaction between supervision and self play in emergent communication"):
  with members:
    - "anti efficient encoding in emergent communication"
    - "emergent linguistic phenomena in multi agent communication games"
    - "capacity bandwidth and compositionality in emergent language learning"
    - "ease of teaching and language structure from emergent communication"
    - "on the pitfalls of measuring emergent communication"
    - "mastering emergent language learning to guide in simulated navigation"
    - "countering language drift via visual grounding"
    - "emergence of compositional language with deep generational transmission"

Connected through hyperedge HE-2 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-3 (induced by source paper "self monitoring navigation agent via auxiliary progress estimation"):
  with members:
    - "neural baby talk"
    - "talk the walk navigating new york city through grounded dialogue"
    - "gibson env real world perception for embodied agents"
    - "speaker follower models for vision and language navigation"
    - "guided feature transformation gft a neural language grounding module for embodied agents"
    - "on evaluation of embodied navigation agents"
    - "end to end dense video captioning with masked transformer"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate unseen environments back translation with environmental dropout"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "reinforced cross modal matching and self supervised imitation learning for vision language navigation"
    - "speaker follower models for vision and language navigation"
    - "on evaluation of embodied navigation agents"
    - "style transfer through back translation"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "understanding back translation at scale"

Connected through hyperedge HE-5 (induced by source paper "spoc search based pseudocode to code"):
  with members:
    - "neural program repair by jointly learning to localize and repair"
    - "frangel component based synthesis with control structures"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "naps natural program synthesis dataset"
    - "mapping language to code in programmatic context"
    - "coarse to fine decoding for neural semantic parsing"
    - "retrieval based neural code generation"

Connected through hyperedge HE-6 (induced by source paper "are you looking grounding to multiple modalities in vision and language navigation"):
  with members:
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "speaker follower models for vision and language navigation"
    - "blindfold baselines for embodied qa"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "look before you leap bridging model free and model based reinforcement learning for planned ahead vision and language navigation"
    - "visual semantic navigation using scene priors"
    - "shifting the baseline single modality performance on visual navigation qa"

Connected through hyperedge HE-7 (induced by source paper "building an application independent natural language interface"):
  with members:
    - "spatialnli a spatial domain natural language interface to databases using spatial comprehension"
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "an end to end neural natural language interface for databases"
    - "explain yourself a natural language interface for scrutable autonomous robots"
    - "mapping natural language commands to web elements"

Connected through hyperedge HE-8 (induced by source paper "run through the streets a new dataset and baseline models for realistic urban navigation"):
  with members:
    - "are we modeling the task or the annotator an investigation of annotator bias in natural language understanding datasets"
    - "learning to map context dependent sentences to executable formal queries"
    - "talk the walk navigating new york city through grounded dialogue"
    - "touchdown natural language navigation and spatial reasoning in visual street environments"
    - "chalet cornell house agent learning environment"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "following high level navigation instructions on a simulated quadcopter with imitation learning"

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)