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: "extracting biomolecular interactions using semantic parsing of biomedical text"
  Abstract: We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sentences (documents); (iii) We further modify and expand a graph kernel learning framework to enable concurrent exploitation of automatically induced AMR (semantic) and dependency structure (syntactic) representations. Our experiments show that our approach yields interaction extraction systems that are more robust in environments where there is a significant mismatch between training and test conditions.

Connected through hyperedge HE-1 (induced by source paper "nearly unsupervised hashcode representations for relation extraction"):
  with members:
    - "exploring semi supervised variational autoencoders for biomedical relation extraction"
    - "stochastic learning of nonstationary kernels for natural language modeling"
    - "kernelized hashcode representations for relation extraction"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-2 (induced by source paper "the materials science procedural text corpus annotating materials synthesis procedures with shallow semantic structures"):
  with members:
    - "an annotated corpus for machine reading of instructions in wet lab protocols"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "playing by the book an interactive game approach for action graph extraction from text"
    - "automatically extracting action graphs from materials science synthesis procedures"

Connected through hyperedge HE-3 (induced by source paper "stochastic learning of nonstationary kernels for natural language modeling"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "learning to skim text"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "learning structural kernels for natural language processing"
    - "effective approaches to attention based neural machine translation"
    - "classifying relations by ranking with convolutional neural networks"
    - "classifying relations via long short term memory networks along shortest dependency path"
    - "task oriented learning of word embeddings for semantic relation classification"

Connected through hyperedge HE-4 (induced by source paper "feature assisted bi directional lstm model for protein protein interaction identification from biomedical texts"):
  with members:
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "end to end relation extraction using lstms on sequences and tree structures"
    - "a dependency based neural network for relation classification"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "kernelized hashcode representations for relation extraction"):
  with members:
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deep learning for extracting protein protein interactions from biomedical literature"
    - "deep variational information bottleneck"
    - "a survey on learning to hash"
    - "orthogonal random features"
    - "generalized exponential concentration inequality for r enyi divergence estimation"
    - "learning structural kernels for natural language processing"
    - "adaptive estimation of shannon entropy"

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)