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: "learning distributed representations of symbolic structure using binding and unbinding operations"
  Abstract: Widely used recurrent units, including Long-short Term Memory (LSTM) and Gated Recurrent Unit (GRU), perform well on natural language tasks, but their ability to learn structured representations is still questionable. Exploiting Tensor Product Representations (TPRs) --- distributed representations of symbolic structure in which vector-embedded symbols are bound to vector-embedded structural positions --- we propose the TPRU, a recurrent unit that, at each time step, explicitly executes structural-role binding and unbinding operations to incorporate structural information into learning. Experiments are conducted on both the Logical Entailment task and the Multi-genre Natural Language Inference (MNLI) task, and our TPR-derived recurrent unit provides strong performance with significantly fewer parameters than LSTM and GRU baselines. Furthermore, our learnt TPRU trained on MNLI demonstrates solid generalisation ability on downstream tasks.

Connected through hyperedge HE-1 (induced by source paper "enhancing the transformer with explicit relational encoding for math problem solving"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "a perspective on objects and systematic generalization in model based rl"
    - "open sesame getting inside bert s linguistic knowledge"
    - "analysing mathematical reasoning abilities of neural models"
    - "systematic generalization what is required and can it be learned"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "learning to reason with third order tensor products"

Connected through hyperedge HE-2 (induced by source paper "rnns implicitly implement tensor product representations"):
  with members:
    - "assessing composition in sentence vector representations"
    - "on the evaluation of semantic phenomena in neural machine translation using natural language inference"
    - "long short term memory as a dynamically computed element wise weighted sum"
    - "relational inductive biases deep learning and graph networks"
    - "learning to reason with third order tensor products"
    - "tree to tree neural networks for program translation"
    - "evaluating compositionality in sentence embeddings"
    - "senteval an evaluation toolkit for universal sentence representations"

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.NE(Neural and Evolutionary Computing)