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 "interactive language learning by question answering"):
  Abstract: Humans observe and interact with the world to acquire knowledge. However, most existing machine reading comprehension (MRC) tasks miss the interactive, information-seeking component of comprehension. Such tasks present models with static documents that contain all necessary information, usually concentrated in a single short substring. Thus, models can achieve strong performance through simple word- and phrase-based pattern matching. We address this problem by formulating a novel text-based question answering task: Question Answering with Interactive Text (QAit). In QAit, an agent must interact with a partially observable text-based environment to gather information required to answer questions. QAit poses questions about the existence, location, and attributes of objects found in the environment. The data is built using a text-based game generator that defines the underlying dynamics of interaction with the environment. We propose and evaluate a set of baseline models for the QAit task that includes deep reinforcement learning agents. Experiments show that the task presents a major challenge for machine reading systems, while humans solve it with relative ease.

Includes members:
  Member "nail a general interactive fiction agent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "learn how to cook a new recipe in a new house using map familiarization curriculum learning and common sense to learn families of text based adventure games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "transfer in deep reinforcement learning using knowledge graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploration based language learning for text based games")
  Member "babyai first steps towards grounded language learning with a human in the loop":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the interaction between supervision and self play in emergent communication")
  Member "textworld a learning environment for text based games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "building dynamic knowledge graphs from text using machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning dynamic knowledge graphs to generalize on text based games")
  Member "hotpotqa a dataset for diverse explainable multi hop question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "the web as a knowledge base for answering complex questions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

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)