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: "tracking discrete and continuous entity state for process understanding"
  Abstract: Procedural text, which describes entities and their interactions as they undergo some process, depicts entities in a uniquely nuanced way. First, each entity may have some observable discrete attributes, such as its state or location; modeling these involves imposing global structure and enforcing consistency. Second, an entity may have properties which are not made explicit but can be effectively induced and tracked by neural networks. In this paper, we propose a structured neural architecture that reflects this dual nature of entity evolution. The model tracks each entity recurrently, updating its hidden continuous representation at each step to contain relevant state information. The global discrete state structure is explicitly modeled with a neural CRF over the changing hidden representation of the entity. This CRF can explicitly capture constraints on entity states over time, enforcing that, for example, an entity cannot move to a location after it is destroyed. We evaluate the performance of our proposed model on QA tasks over process paragraphs in the ProPara dataset and find that our model achieves state-of-the-art results.

Connected through hyperedge HE-1 (induced by source paper "effective use of transformer networks for entity tracking"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "spanbert improving pre training by representing and predicting spans"
    - "attention is not explanation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "everything happens for a reason discovering the purpose of actions in procedural text"):
  with members:
    - "a structured learning approach to temporal relation extraction"
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "what happened leveraging verbnet to predict the effects of actions in procedural text"
    - "simulating action dynamics with neural process networks"
    - "a neural knowledge language model"

Connected through hyperedge HE-3 (induced by source paper "be consistent improving procedural text comprehension using label consistency"):
  with members:
    - "building dynamic knowledge graphs from text using machine reading comprehension"
    - "tracking state changes in procedural text a challenge dataset and models for process paragraph comprehension"
    - "allennlp a deep semantic natural language processing platform"
    - "reasoning about actions and state changes by injecting commonsense knowledge"
    - "text2shape generating shapes from natural language by learning joint embeddings"
    - "simulating action dynamics with neural process networks"
    - "a thorough examination of the cnn daily mail reading comprehension task"
    - "tracking the world state with recurrent entity networks"

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)