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: "addressing the loss metric mismatch with adaptive loss alignment"
  Abstract: In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this assumption by meta-learning an adaptive loss function to directly optimize the evaluation metric. We propose a sample efficient reinforcement learning approach for adapting the loss dynamically during training. We empirically show how this formulation improves performance by simultaneously optimizing the evaluation metric and smoothing the loss landscape. We verify our method in metric learning and classification scenarios, showing considerable improvements over the state-of-the-art on a diverse set of tasks. Importantly, our method is applicable to a wide range of loss functions and evaluation metrics. Furthermore, the learned policies are transferable across tasks and data, demonstrating the versatility of the method.

Connected through hyperedge HE-1 (induced by source paper "learning adaptive loss for robust learning with noisy labels"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "curriculum loss robust learning and generalization against label corruption"
    - "l_dmi an information theoretic noise robust loss function"
    - "learning surrogate losses"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "robust bi tempered logistic loss based on bregman divergences"
    - "joint optimization framework for learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-2 (induced by source paper "online meta critic learning for off policy actor critic methods"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning surrogate losses"
    - "learning to teach with dynamic loss functions"
    - "meta gradient reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "meta reinforcement learning of structured exploration strategies"
    - "on learning intrinsic rewards for policy gradient methods"

Connected through hyperedge HE-3 (induced by source paper "optimizing black box metrics with adaptive surrogates"):
  with members:
    - "optimizing generalized rate metrics through game equilibrium"
    - "learning surrogate losses"
    - "unleashing linear optimizers for group fair learning and optimization"
    - "learning to teach with dynamic loss functions"
    - "constrained classification and ranking via quantiles"
    - "learning to reweight examples for robust deep learning"
    - "online and stochastic gradient methods for non decomposable loss functions"
    - "optimizing f measure a tale of two approaches"

Connected through hyperedge HE-4 (induced by source paper "softadapt techniques for adaptive loss weighting of neural networks with multi part loss functions"):
  with members:
    - "autoloss learning discrete schedules for alternate optimization"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "a general and adaptive robust loss function"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "yolo9000 better faster stronger"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-5 (induced by source paper "a new approach to forecast service parts demand by integrating user preferences into multi objective optimization"):
  with members:
    - "multi task learning as multi objective optimization"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural 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.LG(Machine Learning)