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: "the use of classifiers in sequential inference"
  Abstract: We study the problem of combining the outcomes of several different classifiers in a way that provides a coherent inference that satisfies some constraints. In particular, we develop two general approaches for an important subproblem-identifying phrase structure. The first is a Markovian approach that extends standard HMMs to allow the use of a rich observation structure and of general classifiers to model state-observation dependencies. The second is an extension of constraint satisfaction formalisms. We develop efficient combination algorithms under both models and study them experimentally in the context of shallow parsing.

Connected through hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding"):
  with members:
    - "on the capabilities and limitations of reasoning for natural language understanding"
    - "evidence sentence extraction for machine reading comprehension"
    - "question answering as global reasoning over semantic abstractions"
    - "going on a vacation takes longer than going for a walk a study of temporal commonsense understanding"
    - "cogcomptime a tool for understanding time in natural language text"
    - "annotation artifacts in natural language inference data"
    - "multi granularity hierarchical attention fusion networks for reading comprehension and question answering"
    - "multi hop knowledge graph reasoning with reward shaping"

Connected through hyperedge HE-2 (induced by source paper "question answering as global reasoning over semantic abstractions"):
  with members:
    - "question answering through transfer learning from large fine grained supervision data"
    - "adversarial examples for evaluating reading comprehension systems"
    - "answering complex questions using open information extraction"
    - "question answering via integer programming over semi structured knowledge"
    - "a decomposable attention model for natural language inference"
    - "bidirectional attention flow for machine comprehension"
    - "squad 100 000 questions for machine comprehension of text"
    - "semantic parsing to probabilistic programs for situated question answering"

Connected through hyperedge HE-3 (induced by source paper "partial or complete that s the question"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "cogcomptime a tool for understanding time in natural language text"
    - "understanding autoencoders with information theoretic concepts"
    - "exploiting partially annotated data for temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"
    - "active learning with partial feedback"
    - "structured set matching networks for one shot part labeling"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-4 (induced by source paper "cogcomptime a tool for understanding time in natural language text"):
  with members:
    - "joint reasoning for temporal and causal relations"
    - "a structured learning approach to temporal relation extraction"
    - "a multi axis annotation scheme for event temporal relations"
    - "improving temporal relation extraction with a globally acquired statistical resource"

Connected through hyperedge HE-5 (induced by source paper "discourse structure in machine translation evaluation"):
  with members:
    - "discotk using discourse structure for machine translation evaluation"
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "pairwise neural machine translation evaluation"
    - "more accurate tests for the statistical significance of result differences"

Connected through hyperedge HE-6 (induced by source paper "automated phrase mining from massive text corpora"):
  with members:
    - "scalable topical phrase mining from text corpora"
    - "kea practical automatic keyphrase extraction"

Connected through hyperedge HE-7 (induced by source paper "efficiently inducing features of conditional random fields"):
  with members:
    - "discriminative probabilistic models for relational data"

Connected through hyperedge HE-8 (induced by source paper "exponential families for conditional random fields"):
  with members:
    - "estimators for stochastic unification based grammars"

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.LG(Machine Learning)