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: "task oriented query reformulation with reinforcement learning"
  Abstract: Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upper-bound performance of a model in a particular environment and verify that there is still large room for improvements.

Connected through hyperedge HE-1 (induced by source paper "unsupervised question decomposition for question answering"):
  with members:
    - "break it down a question understanding benchmark"
    - "unsupervised question answering by cloze translation"
    - "compositional questions do not necessitate multi hop reasoning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "self assembling modular networks for interpretable multi hop reasoning"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "huggingface s transformers state of the art natural language processing"
    - "multi hop reading comprehension through question decomposition and rescoring"

Connected through hyperedge HE-2 (induced by source paper "subword language model for query auto completion"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "personalized neural language models for real world query auto completion"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "learning to discover ground and use words with segmental neural language models"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "personalized language model for query auto completion"
    - "distilling knowledge for search based structured prediction"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"

Connected through hyperedge HE-3 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "deep nets what have they ever done for vision"
    - "natural language to structured query generation via meta learning"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"

Connected through hyperedge HE-4 (induced by source paper "multi step retriever reader interaction for scalable open domain question answering"):
  with members:
    - "training a ranking function for open domain question answering"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "efficient and robust question answering from minimal context over documents"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "emergent communication through negotiation"
    - "evidence aggregation for answer re ranking in open domain question answering"
    - "differentiable learning of logical rules for knowledge base reasoning"
    - "fast k nearest neighbour search via prioritized dci"

Connected through hyperedge HE-5 (induced by source paper "generative question refinement with deep reinforcement learning in retrieval based qa system"):
  with members:
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "proximal policy optimization and its dynamic version for sequence generation"
    - "identifying well formed natural language questions"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "memen multi layer embedding with memory networks for machine comprehension"
    - "learning to paraphrase for question answering"
    - "sentence simplification with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator"):
  with members:
    - "model based reinforcement learning for whole chain recommendations"
    - "learning to coordinate multiple reinforcement learning agents for diverse query reformulation"
    - "learning to collaborate multi scenario ranking via multi agent reinforcement learning"
    - "deep reinforcement learning for sponsored search real time bidding"
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "a multi agent reinforcement learning method for impression allocation in online display advertising"
    - "learning to advertise with adaptive exposure via constrained two level reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "dynamic search optimizing the game of information seeking"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "query tracking for e commerce conversational search a machine comprehension perspective"
    - "an algorithmic perspective on imitation learning"
    - "a reinforcement learning driven translation model for search oriented conversational systems"
    - "neural approaches to conversational ai"
    - "top k off policy correction for a reinforce recommender system"
    - "a click sequence model for web search"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"

Connected through hyperedge HE-8 (induced by source paper "meta answering for machine reading"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "answering while summarizing multi task learning for multi hop qa with evidence extraction"
    - "synthetic qa corpora generation with roundtrip consistency"
    - "revealing the importance of semantic retrieval for machine reading at scale"
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a bert baseline for the natural questions"
    - "probing neural network comprehension of natural language arguments"
    - "episodic memory reader learning what to remember for question answering from streaming data"

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.IR(Information Retrieval)