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: "a multimodal anomaly detector for robot assisted feeding using an lstm based variational autoencoder"
  Abstract: The detection of anomalous executions is valuable for reducing potential hazards in assistive manipulation. Multimodal sensory signals can be helpful for detecting a wide range of anomalies. However, the fusion of high-dimensional and heterogeneous modalities is a challenging problem. We introduce a long short-term memory based variational autoencoder (LSTM-VAE) that fuses signals and reconstructs their expected distribution. We also introduce an LSTM-VAE-based detector using a reconstruction-based anomaly score and a state-based threshold. For evaluations with 1,555 robot-assisted feeding executions including 12 representative types of anomalies, our detector had a higher area under the receiver operating characteristic curve (AUC) of 0.8710 than 5 other baseline detectors from the literature. We also show the multimodal fusion through the LSTM-VAE is effective by comparing our detector with 17 raw sensory signals versus 4 hand-engineered features.

Connected through hyperedge HE-1 (induced by source paper "advae a self adversarial variational autoencoder with gaussian anomaly prior knowledge for anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "pyod a python toolbox for scalable outlier detection"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "are generative deep models for novelty detection truly better"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "generative adversarial active learning for unsupervised outlier detection"

Connected through hyperedge HE-2 (induced by source paper "what went wrong identification of everyday object manipulation anomalies"):
  with members:
    - "fast robust and versatile event detection through hmm belief state gradient measures"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "assistive gym a physics simulation framework for assistive robotics"):
  with members:
    - "multidimensional capacitive sensing for robot assisted dressing and bathing"
    - "modeling collaboration for robot assisted dressing tasks"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "habitat a platform for embodied ai research"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "sim to real learning agile locomotion for quadruped robots"
    - "deep haptic model predictive control for robot assisted dressing"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-4 (induced by source paper "deep clustering with intra class distance constraint for hyperspectral images"):
  with members:
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"
    - "trace quotient with sparsity priors for learning low dimensional image representations"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "deep subspace clustering networks"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "unsupervised deep embedding for clustering analysis"
    - "convolutional clustering for unsupervised learning"

Connected through hyperedge HE-5 (induced by source paper "toward active robot assisted feeding with a general purpose mobile manipulator design evaluation and lessons learned"):
  with members:
    - "in home and remote use of robotic body surrogates by people with profound motor deficits"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "tracking human pose during robot assisted dressing using single axis capacitive proximity sensing"
    - "towards assistive feeding with a general purpose mobile manipulator"
    - "reaching in clutter with whole arm tactile sensing"

Connected through hyperedge HE-6 (induced by source paper "visual anomaly detection in event sequence data"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "carepre an intelligent clinical decision assistance system"
    - "anomaly detection using one class neural networks"
    - "adam a method for stochastic optimization"

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.RO(Robotics)